Using the massive database of the Human Phenotype Project, researchers at the Weizmann Institute of Science in Rehovot want to predict disease before it strikes and – they hope – delay or even prevent it.
Before an important meeting or when a big decision needs to be made, we often mentally run through various scenarios before settling on the best course of action. But when it comes to our health – choosing a treatment for an ailment or even selecting a dietary regimen – it’s much harder to predict how each choice will affect our bodies and whether it will suit us personally.
Recently, a large team of researchers harnessed artificial intelligence to create a personalized “digital twin” that allows them to detect a risk of developing diseases, initiate preventive treatment, and even run simulations to predict the treatment that would be most effective.
The team was led by Assistant Prof. Smadar Shilo, a senior pediatrician and pediatric endocrinologist at Schneider Children’s Medical Center in Petah Tikva and a consultant to Prof. Eran Segal’s Computer Science and Applied Mathematics Department laboratory at Weizmann, along with Dr. Lee Reicher of that department.
“Not everybody wants to know what diseases they will contract in the future – and that’s perfectly legitimate,” Shilo told The Jerusalem Post in an interview. “It depends on one’s personality. But many people want this foresight so they’ll be able to prepare for them or even prevent them by changing their lifestyles.”
Israel is perfect place for biobanks due to its diversity
ISRAEL IS the perfect place for biobanks because the population comes from countless ethnic groups concentrated in one place, she said. “There are biobanks in other countries but they don’t have the genetic variety that we do.”
“When we launched the project in Israel in 2018, our initial goal was 10,000 participants,” Segal recalled. “Since then, more than 30,000 people have signed up for inclusion, and we hope to reach 100,000 in the future. To deepen our understanding of ethnic, environmental and cultural variations, we set up a branch in Japan and are currently finalizing the establishment of another in the United Arab Emirates, in collaboration with Prof. Eric Xing from the Mohamed bin Zayed University of Artificial Intelligence.”
The Human Genome Project (HGP) was launched at the Rehovot institute 35 years ago to explore the basic question of what makes each of us who we are. Before that, only a fraction of the human genome was known to science. The project led to the identification of tens of thousands of genes that shape our traits, revealing the genetic basis of numerous diseases.
Today, however, it is clear that genes alone provide only a partial picture.
Many of the characteristics that define us and the diseases that threaten us are linked to environmental factors, the community of microorganisms residing in our bodies (our microbiome), the aging process, and other factors, said Shilo, who is married with three children – a year-old baby, and nine- and 12-year-old sons.
As if she didn’t have enough to handle, she also has a clinical practice at Petah Tikva’s Schneider Children’s Medical Center – Israel’s largest pediatric hospital – where she treats children with hormonal disorders, including type-1 diabetes, obesity, and growth-related conditions. Combining research, patient care, and teaching allows her to test scientific findings in real-world settings and to bring clinical questions back to the lab for further investigation. “I don’t have even one doctor in the family. But I wanted to work with people and do research as well.”
Eager to get a broader picture, Segal launched the HPP in 2018. The project tracks thousands of participants who undergo extensive medical assessments and testing every two years over a 25-year period. These evaluations cover 17 different body systems and include a wide array of tests, such as body measurements, nutritional logs, ultrasounds, bone mineral density tests, voice recordings, home sleep tests, continuous glucose monitoring over two-week periods, gene sequencing, cellular protein analysis, and microbiome analysis of samples from the gut, vagina, and oral cavity.
The project includes longitudinal profiling of variables such as medical history, lifestyle and nutrition, anthropometrics (the scientific study of the measurements and proportions of the human body), blood tests, continuous glucose and sleep monitoring, imaging and integration and analysis of data from multiple sources including genetics, the study of small molecules called metabolites such as cells, tissues, or bodily fluids, immune profiling, and the microbiome.
The team is expanding the participants’ age range; initially, the researchers recruited people between 40 and 70 years of age, but now younger and older people are also joining the study. This research has led to the creation of an advanced database that is not only extensive but also represents the most in-depth collection of human data currently in existence.
“We recognized the importance of sharing this resource with the scientific community and have now made it accessible digitally to research groups worldwide, while maintaining the privacy of the participants,” Segal explained.
“We believe that the data we have compiled will profoundly affect the field of medicine.”
Aging process varies among individuals
MODERN MEDICINE relies largely on conducting tests and comparing the results to the average ranges for a person’s age and sex. However, the underlying health status and the aging process vary considerably among individuals. The research team in Segal’s lab developed an AI model that studies typical physiological changes occurring throughout a person’s lifespan in 17 human body systems and learns to identify deviations from expected patterns. The model is built on a platform developed by Pheno.AI, a company located in Tel Aviv-Yafo specializing in AI research for healthcare.
“The model assigns scores to each body system and compares these values to the expected values for the participant’s chronological age, sex and body mass index,” Segal explained. “Based on the deviation from these predicted values, the model determines the participant’s biological age. The older the apparent age of a body system, the greater the risk of associated diseases.”
The study of biological age has revealed significant differences between the sexes. “While men’s biological age generally increases relatively linearly, we observe an acceleration in women’s biological aging during their fifth decade of life,” Segal noted.
“Menopause is a pivotal event in many medical respects, and it appears to reset the biological age clock. For example, we found that a decrease in bone density is more strongly linked with the time that passed since the onset of menopause than with chronological age, and our measurements make it possible to detect the start of menopause early so that hormonal treatment can be planned accordingly.”
The HPP has also uncovered new avenues for the early diagnosis of a multitude of medical conditions, including
breast cancer, inflammatory bowel disease, and endometriosis. That’s because these conditions are characterized by a change in the composition of the patient’s microbiome, and this change acts as a unique and identifiable “signature,” Segal said.
THE PROJECT’S most significant promise lies in its potential to advance personalized or precision medicine. The researchers aim to achieve this through a unified computer model that will integrate all the information collected from each participant in the project, creating a digital twin of that person. This model – currently under development in a project led by doctoral student Guy Lutsker – will predict what medical events the participant is likely to experience in the future and how best to prevent them.
To train the model, the scientists let it study the medical records of each participant and then ask it to make minor predictions. A specific piece of information is withheld each time, and the model is tasked with predicting it based on the existing data. This training approach helps create a generative AI model that can predict medical events and in the future is expected to construct an entire personalized “health trajectory” outlining a person’s future health status years in advance.
The research team has already developed a model that, by analyzing participants’ glucose levels, has successfully predicted not only their future levels but also which pre-diabetic individuals are at the highest risk of developing diabetes within the next two years. Such predictions help prevent the disease, or delay it at an early stage. The researchers are already using the digital twin to check which dietary changes or drugs would be most beneficial for each participant.
In the future, the model is expected to hold all the information within the database, enabling it to predict a wide range of medical events and spare patients the often-lengthy trial-and-error process of finding the most effective treatment. They are developing an application that will bring all the collected information to the participants’ fingertips and, in the future, will provide them with a personal ‘health trajectory,’” Segal added.
“We are living in an era of incredibly rapid change. The realms of health and medicine will undergo dramatic transformations in the coming years, becoming increasingly AI-driven,” he concluded. “Our project is poised to be a leading global source of information and innovation, and this is all thanks to our participants.”