“I never went through a biological clock experience. I never even heard it ticking.”~Jane Lynch
Well laughing it off may have worked for Comedian Jane Lynch, but for most of us, our biological clocks are not just ticking. They’re pounding.
Every single one of us is on the clock. And we are comprised of the many clocks ticking at different rates at the molecular, cellular, tissue, organ, system, body, physiology and social levels. But one company is hoping its new technology will encourage health-conscious people all over the world to find their weakest clock and repair or rewind it.
A group of scientists who study Artificial Intelligence (AI) say they’ve come up with a process that can not only measure biological age, but tell you whether you will live longer or die younger than other people your age, and how to increase your odds that you will do the former.
They’ve called it the Aging Clock—an aging clock that is embedded in our body’s blood chemistry that forecasts when our cells and bodies are most likely to die and whether we’re getting old too quickly compared with other people our age.
It’s the result of a big-data, AI-driven analysis of blood tests from 130,000 people from South Korean, Canadian and Eastern European patient populations. The results netted a computer algorithm scientists at Insilico Medicine describe as the most precise measure of a person’s biological age. They say the algorithm and corresponding website, www.Young.AI, can provide visitors real time information about their potential life span and hopefully help them lengthen it.
“Our test gives people a sober look at how fast or slow their biological clock is ticking,” explained Alex Zhavoronkov, PhD, CEO and founder of Insilico Medicine. “And for those who learn that their bodies are aging at a fast, unhealthy rate, the test will hopefully serve as a wakeup call, convincing them to take steps now that will add years to their life later—all this insight from a blood test.”
Zhavoronkov said that while most of us tend to think of age as the number of birthdays we’ve celebrated, scientists agree this metric, also known as our chronological age, is not the most accurate predictor of our mortality or how long we can expect to live. What’s more, that figure could be off as much as 30 years.
“A far more accurate predictor is our biological age, which measures how quickly the cells in our body will deteriorate compared with the general population,” he said. “Depending on the genetics we inherit and the lifestyle choices we make regarding diet, exercise, weight, stress and habits like smoking or drinking, our biological age can vary as much as 30 years compared with our chronological age.”
He said it explains why we sometimes meet a grey-haired, wrinkled person who looks older than what their driver’s license says. But it also explains why a healthy-looking 60-year-old may have the body of a 40-year-old. He said his technology could help each of us on the road to end up like the latter. “While it’s well known that blood tests are used to diagnose disease and monitor our health, they can now be used to give us a preview of what lies ahead,” Zhavoronkov said.
“Because of its value in predicting our mortality, scientists have tried for years to discover a precise formula to measure our biological age – a true aging clock,” Zhavoronkov said. “Such a formula would help them better understand how the aging process speeds up or slows down compared to our chronological age and how customized medical interventions can help us live longer, by effectively slowing down our clock.”
Zhavoronkov said the analysis of blood tests done by Insilico Medicine on 130,000 South Koreans, Canadians and Eastern-Europeans and reported in the Journals of Gerontology, is the largest pool of blood work ever used in a longevity study.
"A lot of money has been spent in recent years to identify the precise biomarkers of aging. These attempts have largely failed,” said Polina Mamoshina, a senior research scientist at Insilico Medicine. “But today, thanks to AI and the incredibly fast computational power of our deep learning, neural networks, we can discover patterns and formulas in a huge pool of blood work that could not be discovered just a few years ago.”
Zhavoronkov said each of the 130,000 blood tests in the study was analyzed for 21 parameters typically measured in a blood sample including cholesterol, inflammation markers, hemoglobin count, albumin levels and 17 other chemical variants. “By using AI to analyze and compare the blood chemistry, age, ethnicity, and other data from so many thousands of people in single study, researchers created a computer algorithm scientists regard as the first truly reliable aging clock for humans. The formula, when applied to data in a single drop of blood, generates a reliable forecast regarding how long we can expect to live and whether we’re aging prematurely compared with our chronological age.
He said the results were in line with the hypothesis that “ethnically-diverse aging clocks have the potential to predict chronological age and quantify biological age with greater accuracy than generic aging clocks,” and that further, they have a greater capacity to explain the confusing and often surprising effect of ethnic, geographic, behavioral and environmental factors on the prediction of chronological age and the measurement of biological age.
In a research paper published last month by Oxford University Press on behalf of The Gerontological Society of America describing the study and the aging clock, Insilico scientists state that deep learning-based hematological aging clocks, “even when trained on a limited feature space, demonstrate reasonably high accuracy in predicting chronological age…Indeed, going forward we will include additional population specific blood biochemistry datasets in order to further increase the predictive power and general utility of DL-based hematologic aging clocks...”
According to Insilico scientists, the algorithm will also be useful in clinical trials for anti-aging medications because it will allow researchers to measure the efficacy of a drug by observing whether a patient taking it shifts from an advanced-aging, higher-risk status to a healthy, lower-risk one.
“Every living being has age. It is the one universal feature that unites all of us, but we are all different in many ways such as age, whether we will have cancer or diabetes, whether we are male or female. When we train deep neural networks (DNNs) on age, they learn a lot about biology,” Zhavoronkov said. “We try to train the DNN with as many examples as possible – race, ethnicity, diet. When we process data from millions of clinical blood tests, we are training the AI to predict a patient’s age. When we train the DNNs, we train them on healthy people, so those predictors become predictors of not only age but optimal health. Then we test those predictors on people with health problems and try to see if those people are predicted older or younger than their chronological age.”
Think of it as looking in a hypothetical electronic mirror. Today it says you look 60 years old. It recognizes wrinkles, dark spots, etc. Tomorrow, you remove those visible characteristics that added age in the mirror’s estimation. Then take a look again. Now it says you’re five years younger. If you do it on a blood test, it works the same. Maybe diet or exercise predicts how lifestyle can affect perceived age to the DNN.
Zhavoronkov said several apps already exist that imitate what the Aging Clock does. Though they’re entertaining, Zhavoronkov says they’re wasting precious resources that could be used on aging and disease research. “They are already out there. Many of those cool apps that show you how you will look as a woman or a man—those are deep learning. Some can be achieved using Generative adversarial networks (GANs). They create or imagine that circumstance and show it to you. It’s wasting people’s time and computing resources,” he said. “We want to build accurate biological age over time and identify predictors to make you look younger and to prevent diseases.”
Part of a broader family of machine learning methods, deep learning is based on learning data representations, as opposed to task-specific algorithms. Deep learning has made progress within a number of disciplines in recent years. We see machine learning in computer science programs, industry conferences and all over the news. Algorithms can now even teach themselves to play games.
Deep learning algorithms in medicine are trained on databases of medical images to spot life-threatening disease with equal or greater accuracy than human professionals, according to Jason Dorrier of SingularityHub.com. “There’s even speculation that AI, if we learn to trust it, could be invaluable in diagnosing disease.”
Zhavoronkov believes with more applications and a longer track record that trust is coming. And with estimates that 71.4 million people will be age 65 or older and make up some 20 percent of the U.S. population by 2029, it can’t happen soon enough.
But everyone will have to work together, he says. “The war on aging is not a war that can be fought by a single person, institution, organization or even country,” he said. “It requires a massive collaborative effort because the process is immensely complex.”
Individuals interested in knowing their biological age can visit the www.Young.AI, where, after they subscribe for a free aging analysis, will be asked to upload at least 18 parameters that appeared in their latest blood test, including Albumin levels, Glucose and 16 other data points. In addition, subscribers will be asked to upload a facial photo, allowing another Insilico AI-driven algorithm, one that recognizes signs of aging in photographs, to make the user’s biological aging estimate even more precise. The report—that appears within seconds after blood work data and the user’s photo are uploaded to the website—is free.
Zhavoronkov said people need not worry about the information they upload to Young.AI. “This information is very low value and safe,” he said. “We don’t ask for sensitive, private information. Actually it’s less than what people put on Facebook. We can’t identify a person by the information you put in.” Though, if you upload pictures, he encourages you to use a nickname rather than your real name.
