Grounded in state of the art research
January AI has assembled a multidisciplinary team of computational, food, translational, and medical scientists to tackle unmet needs in people with or at risk for developing diabetes. Our multi-omic approach synthesizes wearable, food, and microbiome data to make personalized recommendations and products to address hyperglycemia.
January’s AI-derived innovations include nutritional labels for local restaurants and glycemic index for 16 million grocery items, recipes, and menus.
Explore our science
Using heart rate and continuous glucose monitors, our Sugar Challenge Study associated the glycemic responses of 1,022 people on the diabetes spectrum with the glycemic load of the foods they ate. The findings led to January’s proprietary algorithms that accurately predict glucose response for 33 hours in participants with type 2 diabetes. Over 70% of all participants improved their time-in-range (TIR)—the amount of time their blood glucose remained within a healthy zone—including 58% of individuals with the highest blood glucose levels, with a median of 13%.
Meet Michael Snyder
Dr. Snyder received his Ph.D. training at the California Institute of Technology and carried out postdoctoral training at Stanford University. He is a leader in the field of functional genomics and proteomics, and one of the major participants of the ENCODE project. Snyder Lab was the first to perform a large-scale functional genomics project in any organism, and has developed many technologies in genomics and proteomics. These include the development of proteome chips, high resolution tiling arrays for the entire human genome, methods for global mapping of transcription factor binding sites (ChIP-chip now replaced by ChIP-seq), paired end sequencing for mapping of structural variation in eukaryotes, de novo genome sequencing of genomes using high throughput technologies and RNA-Seq. These technologies have been used for characterizing genomes, proteomes and regulatory networks.
Seminal findings from the Snyder laboratory include the discovery that much more of the human genome is transcribed and contains regulatory information than was previously appreciated, and a high diversity of transcription factor binding occurs both between and within species.
Digital Health: Tracking Physiomes and Activity Using Wearable Biosensors Reveals Useful Health-Related Information
Glucotypes reveal new patterns of glucose dysregulation
A longitudinal big data approach for precision health
Personal aging markers and ageotypes revealed by deep longitudinal profiling
Deep longitudinal multiomics profiling reveals two biological seasonal patterns in California
Pre-symptomatic detection of COVID-19 from smartwatch data
Heart Rate and CGM Feature Representation Diabetes Detection From Heart Rate: Learning Joint Features of Heart Rate and Continuous Glucose Monitors Yields Better Representations
LECTURES & INTERVIEWS
Using your genome sequence and big data to manage your health
Fitbit detecting oncoming sickness
Future of individualized medicine
Found My Fitness Podcast with Rhonda Patrick
Moneyball Medicine Podcast: Noosheen Hashemi on Personalized Tech for Blood Glucose Control
Moneyball Medicine Podcast: Michael Snyder on Using Big Data to Keep People Healthy
Members of our Scientific and AI Advisory Boards include top experts in machine learning, nutrition, immunology, and microbiology from Carnegie Mellon, Harvard, Tufts, Stanford, and UC Berkeley.
Scientific Advisory Board
AI Advisory Board