Sarah Urbut: Predicting Your Health Arc

Summary

Sarah Urbut is a physician-scientist at Harvard Medical School and the Massachusetts General Research Institute. She and her colleagues published a landmark paper in Nature on patient health journeys for 3 different cohorts—the Mass General Brigham patients, the UK Biobank, and the All of Us research program—with a cumulative 683,000 individuals, 348 diseases, and up to 52 years of follow-up. Integrating electronic medical records and polygenic risk scores (N=36). An AI model was developed and validated called ALADYNOULLI, with each individual represented latent disease signatures (N=21), mathematically and biologically driven. An example of a patient’s dynamic life journey (health arc) is provided in the Figure below (adapted from the paper, made with NotebookLM). The longitudinal story for each individual is key here. Using a Gaussian process, the risk continually shifts like a GPS navigation when you make a wrong turn and you are re-routed. Considerable effort was dedicated to addressing selection bias with inverse probability weighting. Share Ground Truths Here are 20 temporal pattern signatures for the UK Biobank cohort, which help to provide the biologic mechanism of a person’s disease, as shown for multiple conditions like heart attack, breast cancer, and depression. That is, the same disease phenotype can link to different genomic pathway underpinnings. An example for breast cancer could be related to signature 8 or signature 7; the latter tied to inflammation and metabolic abnormalities. This biological pattern could also be used to detect likely medication failure (such as SSRI treatment for depression) and predict rare diseases. Subscribe now In fact, the model was used to discover genomic signatures for different diseases, as shown below. This model predicted 1 and 10-year risk of diseases at the individual level and far surpassed routinely used clinical prediction calculators such as the pooled cohort equation (PCE) for coronary artery disease (0.89 vs 0.68, respectively, 1-year risk ) and the GAIL model for breast cancer (0.783 vs 0.54, respectively). ALADYNOULLI also surpassed accuracy for precision when compared with the recent Delphi-2M model that I wrote about here The ALADYNOULLI paper is packed with information and equations and is not easy to get through without a sophisticated math background. That’s why the summary infographic below (…