Cardiovascular disease is the leading cause of death in women. There are risk factors specific to women such as pregnancy complications and menopausal status that may play a role in the development of cardiovascular disease in women. The American Heart Association (AHA) recently launched the Research Goes Red registry in collaboration with Verily’s Project Baseline. Verily is a corporation owned by Alphabet (Google’s parent company). The Research Goes Red registry is a novel online platform designed to be participant-focused. Dr. Brooke Aggarwal is Principal Investigator of the Research Goes Red Weight Study, a new 1-year prospective observational study to determine links between weight changes and novel physiologic and behavioral outcomes related to cardiovascular disease in women. The study, a collaboration between AHA and Verily, is conducted primarily remotely. Online questionnaires are completed via an app, and tailored to optimize the experience for research participants. Participants log their waist circumference, body composition via scale, and home blood pressure via the app. An in-person visit to a local LabCorp facility is required to obtain blood samples and blood pressure measurements. All data is synced into the AHA-Verily online platform for the study team to download files with coded data, in real-time. This study is the first of its kind to demonstrate the feasibility of collecting data from the Research Goes Red registry in a decentralized way. We have a rich dataset ready to be analyzed among 300 women across the US, including markers associated with adverse weight gain: body composition, glucose, lipids, insulin resistance, inflammation (C-reactive protein, interleukin-6), as well as correlates of weight gain during the menopausal transition including meal timing, meal frequency, reproductive history, physical activity, perceived stress, and sleep patterns.
Cardiovascular disease remains the leading cause of death in women. There are risk factors specific to women such as pregnancy complications, menopausal status, emotional stress, and sleep quality which play a role in the development of cardiovascular disease in women. In 2019, the American Heart Association (AHA) launched the Research Goes Red registry in collaboration with Verily’s Project Baseline. Verily is a corporation owned by Alphabet (Google’s parent company). The Research Goes Red registry is a novel online platform designed to be participant-centric and highly customizable and scalable. Dr. Brooke Aggarwal is Principal Investigator of the Research Goes Red Weight Study, a new 1-year prospective observational study to determine links between weight changes and novel physiologic and behavioral outcomes related to cardiovascular disease in women. The study, a collaboration between AHA and Verily, is conducted primarily remotely; online questionnaires are completed via the Trialkit app, developed and tailored to optimize the experience for research participants. Participants log their waist circumference, body composition via scale, and home blood pressure via the app. An in-person visit to a local LabCorp facility is required to obtain blood samples and blood pressure measurements. All data and electronic consents collected on the Trialkit app are synced into the AHA-Verily online platform for the study team to download files with coded data, in real-time. This study is the first of its kind to demonstrate the feasibility of collecting data from the Research Goes Red registry in a decentralized way. We have a rich dataset ready to be analyzed among 300 women across the US, including markers associated with adverse weight gain: body composition, glucose, lipids, insulin resistance, inflammation (C-reactive protein, interleukin-6), as well as correlates of weight gain during the menopausal transition including meal timing, meal frequency, reproductive history, physical activity, perceived stress, and sleep patterns.
Background: Genomes are inextricably tied to life as we know it, encoding all the molecular information used by organisms. Next-generation DNA sequencing has resulted in the scalable reading of genomes from organisms that inhabit complex environments - rather than being limited to organisms typically studied in the lab. Alongside this, algorithmic development is beginning to reveal the complex biology of genomes.
Despite identification of modifiable behavioral targets for childhood obesity prevention, prevalence of obesity remains historically high in the United States and the most severe forms are increasing among young children. Disparities in obesity have been exacerbated in the wake of the COVID-19 pandemic. The goal of this project is to examine the effects of social needs (eg food insecurity, housing needs), neighborhood factors, and use of social service referrals on childhood obesity before and after COVID-19. The data scholar will gain hands-on experience with data cleaning, merging, and visualizations. If appropriate to skill level, checking code and conducting analytic tests may be part of the learning experience. Opportunities for abstract and manuscript co-authorship may be possible for exceptional Scholars.
In this project, we aim to develop GAN based models to predict spatio-temporal evolution using open human mobility datasets – SafeGraph (https://www.safegraph.com/).
In this project, we aim to develop GAN based models to predict spatio-temporal evolution using open human mobility datasets – SafeGraph (https://www.safegraph.com/).
This study aims to determine the effect of structural racism on cognitive aging. We are looking at many different aspects of structural racism (civics, education, employment, environment, healthcare, income/credit/wealth, media/marketing, neighborhood factors, and policing), and several variables to measure each aspect. We will be acquiring several large data sets that have data from multiple years. We will be linking all these datasets to determine exposure to structural racism based on geographic location in the United States over the years. We will then link this to a longitudinal dataset with participants’ residences over their lifetime as well as measures of cognitive aging. Our analysis will primarily employ Structural Equation Modeling, but we will also conduct factor analyses and psychometric analyses. We will be analyzing each aspect individually, and as part of a larger model.