The digital twin may predict your patient’s future health

Physicians mentally run through many algorithms before settling on the best course of action for their patients. But when it comes to our health, like selecting a dietary regimen, it is a lot harder to predict how each choice will affect our bodies and whether it will suit us emotionally and psychologically. Recently, researchers at the Weizmann Institute in Israel have developed a method that can detect disease risk, initiate preventive treatment, and even run simulations to predict which treatment will be most effective. These researchers believe they are on the cusp of predicting disease before it causes symptoms or before the condition can be detected on imaging studies or lab tests.

Before the Human Genome Project was launched in 1990 to explore the fundamental question of what makes each of us different and unique, only a fraction of human genes were known. The project identified thousands of genes that determine our traits and revealed the genetic basis of numerous diseases. However, genes only provide a partial picture. Many of the characteristics that characterize us and the diseases that threaten us are linked to surrounding conditions, the microorganisms residing in our microbiome, and the aging process. To gain a broader perspective, the Human Phenotype Project was launched in 2018. This 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 tests such as body measurements, nutritional logs, bone mineral density, voice recordings, home sleep monitoring, continuous glucose monitoring, gene sequencing, cellular protein analysis, and microbiome analysis from the gut, vagina, and oral cavity.

Modern medicine largely relies on testing and comparing results with age-and sex-specific reference ranges. However, the health status and the aging process vary considerably among individuals. They have an AI model that analyzes typical bodily changes across 17 body systems across an individual's lifespan. This helps to identify variations from anticipated normal patterns. The model is built on a platform developed by Pheno.AI, a company specializing in AI research for healthcare. Their model assigns scores to each body system and compares them with the expected values for the participant’s chronological age, sex, and body mass index. Based on deviations from these predicted values, the model estimates the participant’s biological age. The researchers concluded that the older the apparent age of a body system, the greater the risk of associated diseases.

For instance, by tracking participants’ glucose levels, they determined the expected rate of increase in blood sugar for men and women. Their model detects any deviation from this pattern and successfully identifies pre-diabetes in 40% of people who were classified as healthy by conventional testing methods.

The study of biological age has uncovered substantial 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.

Menopause is an uncomfortable event for many middle-aged women, and menopause appears to reset the biological age clock. For example, a decrease in bone density is more strongly correlated with the time since menopause onset than with chronological age. Furthermore, measurements make it possible to detect the start of menopause, so that hormonal treatment can be planned accordingly.

The Human Phenotype Project has also uncovered novel paths for the early diagnosis of a multitude of medical conditions, including breast cancer, inflammatory bowel disease, and endometriosis. That’s because these conditions are marked by alterations in the patient’s microbiome, which serve as a unique and identifiable signature or fingerprint.

Still, the greatest potential of the Human Phenotype Project resides in advancing personalized or precision medicine. Investigators intend to achieve this through a unified computer model that unifies all information collected from each participant in the project, creating a digital twin of that person. This model will predict what medical events the participant is likely to experience in the future and how best to prevent them. The model studied each participant's medical records and then made predictions. A specific piece of information is withheld each time, and the model is tasked with predicting it based on the existing data. This creates a generative AI model that can predict medical events and, in the future, is expected to create an entire personalized “health trajectory” outlining a person’s future health status.

The research team has already developed a model that, through analyzing participants’ glucose levels, has successfully predicted not only their future glucose 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. Moreover, the researchers are already using the digital twin to check which dietary changes or drugs would provide the greatest benefit to each participant. In the future, the model is expected to encompass all the information within the database, enabling it to predict a wide range of medical events and spare patients the lengthy trial-and-error process of finding the most effective treatment.

This achievement is primarily made possible by the community of participants in the Human Phenotype Project. They are developing an application that will make the collected information readily available to patients and providers and, in the future, will provide them with a personal health trajectory. The future of health and medicine will undergo dramatic transformations in the coming years, becoming increasingly AI-driven.

Bottom Line: This research from Israel has led to the creation of an extensive database that represents the most in-depth collection of human data in existence. Healthcare is entering an era of rapid change. Medicine will undergo dramatic AI-driven transformations. The digital twin is positioned to be a leading source of information and innovation to improve our patients' healthcare.

BTW, an excellent infographic on The Digital Twin is available here.

Neil Baum, M.D., is a professor of clinical urology at Tulane University in New Orleans. Baum is the author of several books, including the best-selling book “Marketing Your Medical Practice-Ethically, Effectively, and Economically,” which has sold over 225,000 copies and has been translated into Spanish.