Our research

We are using digital health monitoring tools and network modeling to understand the determinants of cardiometabolic disease.

Many of our research projects also involve multi-omic datasets in large cohort studies, including the Framingham Heart Study (a multi-generational family study with continuous data collection from 1948-today). The unique data resources we are using will help us to uncover the biological mechanisms linking lifestyle behaviors with cardiometabolic disease.

Current projects:

• CGM-based glucotype discovery and characterizing metabolic heterogeneity with meal challenges, hormonal and multi-omic profiling using AI/precision nutrition approaches for early risk detection and for developing scalable interventions.
• Investigating glucose dynamics as a central metabolic biomarker of hepatic, cardiovascular, and neurobiological dysfunction across community-based cohorts to address population health disparities.
• Passive and continuous digital phenotyping of brain aging, including walking cadence, activity patterns, sleep, autonomic function, and glucose stability.
• Personalized causal discovery, prediction, and guidance using wearable mobile health data

Digital health tools currently in use:

• Continuous glucose monitoring (Dexcom and Abbott Libre products)
• Physical activity monitoring (accelerometers including Fitbit, Apple Watch, Actical, smartphone apps)
• Digital diet assessment (ASA24, Keenoa smartphone app, RedCap digital FFQ)

Multi-omic data sources we use:

• Genetic and Epigenetic data
• Metabolomics and Proteomics
• Gut Microbiome