Dissecting causal role of insomnia in cardiovascular disease
This project aims to identify genes and pathways at GWAS loci for insomnia symptoms in humans, test the consequence of loss-of-function of Drosophila orthologs on sleep and cardiac function, and test the impact of perturbed sleep on cardiovascular function in humans and Drosophila.
Linking circadian system and type 2 diabetes through melatonin and receptor variation
This recall by genotype study aims to test the impact of melatonin and MTNR1B variation on regulation glucose regulation in a highly controlled in-laboratory setting and ex vivo in pancreatic islets.
Genetic links between chronic fatigue and long COVID
Genetic approaches leveraging large biobanks offer opportunities for gene discovery unbiased to known biology that should provide insights into the biological underpinnings and possible genetic links with long COVID.
Circadia Study is a novel direct-to-participant cohort study focused on the genetics of individuals with advanced and delayed sleep phase disorder.
SHIFT Study – Impact of melatonin, food timing, and receptor variant on type 2 diabetes
This project aims to test the impact of melatonin and MTNR1B variation on glucose control and risk of type 2 diabetes in an observational study of night shift workers and natural late-night eaters.
This study utilizes the Mass General Brigham Biobank to assess the relationship between sleep timing, sleep disorders, and delirium.
Validating Circadian Rhythm Sleep Wake Disorders Using Machine Learning
We are using machine learning approaches to identify potential cases of Circadian Rhythms Sleep Wake Disorders.
Bipolar/Depression and Circadian Disturbance
We are exploring the extent to which light sensitivity impacts depression through the circadian rhythms.
NF1 Sleep Study – Longitudinal, objective measurement and analysis of sleep-wake patterns in patients with neurofibromatosis type 1 (NF1)
This is an ongoing cohort study to measure sleep and circadian disturbances in adult patients and matched controls.
Sleep metrics from machine learning for Alzheimer’s disease diagnostics
This proposal aims to develop and validate deep learning tools to discover sleep-based predictors for Alzheimer’s disease through analysis of sleep, activity, and cognitive data.