Research Projects Directory

Checkpoint Inhibitors

How many patients taking checkpoint inhibitors develop hypothyroidism after initiating therapy? This question will help clinicians understand the likelihood of potential thyroid complications associated with checkpoint inhibitor therapy. In addition, it can guide diagnoses and treatment of patients and result…

Scientific Questions Being Studied

How many patients taking checkpoint inhibitors develop hypothyroidism after initiating therapy?

This question will help clinicians understand the likelihood of potential thyroid complications associated with checkpoint inhibitor therapy. In addition, it can guide diagnoses and treatment of patients and result in more comprehensive patient care.

Project Purpose(s)

Disease Focused Research (hypothyroidism)

Scientific Approaches

We plan to use the All of Us research database to stratify patients by treatment with a checkpoint inhibitor and development of hypothyroidism after treatment initiation. We will evaluate data from 2012-2018 and calculate the incidence.

Anticipated Findings

We anticipate finding a higher incidence of hypothyroidism in patients on checkpoint inhibitor pharmacotherapy. These findings will help clinicians identify potential complications of treatment which will allow them to properly address concerns.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Nia Nikkhahmanesh - Graduate Trainee, University of Arizona

Collaborators:

Jiali Ling - Project Personnel, University of Arizona

Spectrum of tumors after breast cancer Dataset V4

Interested to know what secondary or recurrent tumors developed after breast cancer and are there any demographic/behavioral/medications/medical conditions that modify/influence the risk of secondary tumors (recurrent/new tumors)

Scientific Questions Being Studied

Interested to know what secondary or recurrent tumors developed after breast cancer and are there any demographic/behavioral/medications/medical conditions that modify/influence the risk of secondary tumors (recurrent/new tumors)

Project Purpose(s)

Disease Focused Research (breast cancer)
Population Health
Other Purpose (Interested to know what secondary or recurrent tumors developed after breast cancer and are there any demographic/behavioral/medications/medical conditions that modify/influence the risk of secondary tumors (recurrent/new tumors))

Scientific Approaches

Plan to
Create a cohort of all participants diagnosed with breast cancer
This is case only study
For the above cohort collect all tumors that happened after diagnosis of breast cancer
Use cox model in order to determine the risk of subsequent tumors (outcome will be time to the second tumor)
Since the range of second/recurrent tumors is large we propose to look first at the spectrum of tumors and then do analyses for specific second tumors like a recurrence
Need to examine what are the factors associated with the second tumor

Anticipated Findings

We anticipate to determine guidelines for risk of second tumors after diagnosis of breast cancer

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Steven Hiek - Project Personnel, University of California, Irvine
Argyrios Ziogas - Late Career Tenured Researcher, University of California, Irvine

Collaborators:

Kathryn Campbell - Project Personnel, University of California, Irvine

Disorders of Consciousness

To investigate disorders of consciousness after brain injury, including neurorehabilitative, neuroethical and neuroscientific dimensions.

Scientific Questions Being Studied

To investigate disorders of consciousness after brain injury, including neurorehabilitative, neuroethical and neuroscientific dimensions.

Project Purpose(s)

Disease Focused Research (Disorders of Consciousness)
Population Health
Social / Behavioral
Ethical, Legal, and Social Implications (ELSI)

Scientific Approaches

Mixed methods will be employed to investigate disorders of consciousness after brain injury, including neurorehabilitative, neuroethical and neuroscientific dimensions.

Anticipated Findings

Mixed methods will be employed to investigate disorders of consciousness after brain injury, with the aim of shedding light on underexplored neurorehabilitative, neuroethical and neuroscientific dimensions.

Demographic Categories of Interest

Research Team

Owner:

Michael Young - Research Fellow, Mass General Brigham

Calculate CRC PRS

We are developing trans-ethnic polygenic risk scores (PRS) for colorectal cancer (CRC). We want to observe and compare the distribution of the PRS in different ancestries, using the genetic data from participants in AllofUs. Using this information, we may be…

Scientific Questions Being Studied

We are developing trans-ethnic polygenic risk scores (PRS) for colorectal cancer (CRC). We want to observe and compare the distribution of the PRS in different ancestries, using the genetic data from participants in AllofUs. Using this information, we may be able to adjust the scores for ancestry groups (Normalize) so that the PRS is comparable across ancestries and provides the same clinical benefits across ancestries.

Project Purpose(s)

Methods Development

Scientific Approaches

We plan to use the AllofUs genotype array data. We will attempt to impute fine scale genotypes genome-wide and then calculate the CRC PRS for each participant. We will also perform principal components of ancestry to determine ancestries for each participant. We plan to use R, the Michigan Imputatation Server, TopMed reference genotype data, and 1000 genomes reference genotype data.

Anticipated Findings

We plan to calculate mean and standard deviation of the PRS within ancestries. We will use this information to adjust PRS values so that they are comparable across ancestries and have similiar clinical utility across ancestries.

Demographic Categories of Interest

Research Team

Owner:

Elisabeth Rosenthal - Project Personnel, University of Washington

Drug repurposing for osteoarthritis

There is a great need for new drug development for osteoarthritis (OA). Some drugs and biologics already in use to treat other conditions may be effective for OA. Identifying which of these already approved drugs may have effects on OA…

Scientific Questions Being Studied

There is a great need for new drug development for osteoarthritis (OA). Some drugs and biologics already in use to treat other conditions may be effective for OA. Identifying which of these already approved drugs may have effects on OA can help reduce the cost and time needed for development of new OA therapies. Our study aims to identify one or more promising treatments for OA by assessing whether certain drug classes may have effects on OA in a large, diverse dataset.

Project Purpose(s)

Disease Focused Research (osteoarthritis)
Ancestry

Scientific Approaches

We plan to use drug epidemiology approaches to determine whether certain drug classes affect OA. When genetic data are available, we will use Mendelian randomization approaches to determine whether variation in genes that mimic the effects of these drug classes are associated with OA.

Anticipated Findings

Our study will provide insights into new drug classes that may be repurposed for OA. We anticipate that our findings will help accelerate drug development for OA by identifying promising new drug targets that can be used for future development of OA therapeutics.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Michelle Yau - Research Associate, Hebrew SeniorLife

SDOH & COVID

The impact of COVID-19 is particularly severe on older adults. The older adults (>= 65yr old) account for more than 80% of the mortality. Other reported negative outcomes include anxiety, depression, poor sleep quality and physical inactivity. We propose to…

Scientific Questions Being Studied

The impact of COVID-19 is particularly severe on older adults. The older adults (>= 65yr old) account for more than 80% of the mortality. Other reported negative outcomes include anxiety, depression, poor sleep quality and physical inactivity. We propose to utilize this unique, rich set of data to study the relationship between social determinants of health (SDOH) and COVID impact on older adults’ behavior and mental health. Our overall hypothesis is that a comprehensive set of SDOH is associated the COVID impact. Our specific aims are: Aim 1. Identify SDOH factors that are significantly associated with COVID impact on older adults’ behavior, adjusting for age and comorbidities; Aim 2. Identify SDOH factors that are significantly associated with COVID impact on older adults’ mental health, adjusting for age and comorbidities.

Project Purpose(s)

Social / Behavioral

Scientific Approaches

We will use a logistic regression model as the primary approach, to describe and examine whether patients with certain SDOH will have a lower rate of COVID-related behavior (including hand washing and social distancing) and higher prevalence rates of mental health concerns. Multivariable models will adjust for potential confounding factors. In addition, given the multiple factors to be examined (i.e., SDOH, age, and comorbidities), the final models will consider controlling multiplicity to avoid inflated Type I error using Hochberg or false discovery rate method. Besides, we will complement the statistical analysis with explainable deep learning modeling. We will train deep transformer model (type of DNN designed for sequence data) to identify the level of association between SDOH and COVID experience. The DNN approach will allow us to assess the impact and significance of each variable in the model using a DNN as well as the interactions between factors.

Anticipated Findings

This study will help understand how SDOH factors affects COVID-related behavior and mental health issues. The findings will help identify risk factors and provide the study basis for the potential preventive strategies for the negative behavior or mental health among the old population.

Demographic Categories of Interest

Research Team

Owner:

Yijun Shao - Research Fellow, George Washington University
Yan Cheng - Other, George Washington University

Collaborators:

Qing Zeng - Late Career Tenured Researcher, George Washington University
Youxuan Ling - Graduate Trainee, Boston University
Phillip Ma - Project Personnel, George Washington University

Disparities in maternal mortality and morbidity in the USA

Maternal health is an important part of the health system of any country. Wit U.S, maternal mortality and morbidity is higher compared to any other developed country. According to PMSS report maternal death rate is 17.3 per 100,000 live birth.…

Scientific Questions Being Studied

Maternal health is an important part of the health system of any country. Wit U.S, maternal mortality and morbidity is higher compared to any other developed country. According to PMSS report maternal death rate is 17.3 per 100,000 live birth. The World Health Organization (WHO) report says the position of the U.S in maternal mortality ranking is 56, which is unacceptable for a developed country. A clear picture of disparity is present in every report dealing this topic. The mortality rate among black American women is about 3 times higher than Non-Hispanic white women. The death rate among other minorities like Non-Hispanic American Indian or Alaska Native, Asian-Pacific Islander is also higher. The case of maternal morbidity is also not different. The maternal death rate among Hispanic-Whites are lower, however Severe Maternal Morbidity (SMM) is higher among this minority group. Most of this mortality and morbidity are avoidable if the women get timely care or treatment.

Project Purpose(s)

Population Health
Social / Behavioral
Educational

Scientific Approaches

With the time-stamped data for different procedures, laboratory results, and other hospital visits for the patient cohort, we aim to develop a process mining algorithm to identify variations in care pathways that cause adverse maternal outcomes. Process mining approaches in healthcare to identify variability in system level factors is a newer approach to conduct disparity research. Our research will address this gap in literature.
We hope to address the potential stigmatization issues by educating necessary stakeholders including hospital, providers, and policymakers. Once we have a preliminary framework, we hope to conduct a community based participatory research and engage with the community members. We propose that the process mining approach would help providers identify the “hotspots” in the care pathways that cause disparities.

Anticipated Findings

The major factors causing maternal mortality and morbidity are sociodemographic, socioeconomic, provider factors and system level factors. This research investigates the system level factors that can cause disparity in maternal health. With the AllofUs data we are trying to group the women utilized the healthcare system for their maternal care, with respect to their race/ethnicity, pregnancy complications, outcome etc. and find out the factors that caused adverse pregnancy outcome, mortality, and morbidity. Moreover, we apply novel process mining approaches to map the patient cohort and identify any changes in care pathways that may result in disparities.

The research will be helpful to find out the system-level factors other than income, insurance, or social status causing disparity in maternal health. Also, it can help in reducing those factors that have a major role in maternal health care disparities.

Demographic Categories of Interest

Research Team

Owner:

Prajina Edayath - Graduate Trainee, University of Texas at El Paso

Collaborators:

Sreenath Chalil Madathil - Early Career Tenure-track Researcher, University of Texas at El Paso
Myrtede Alfred - Other, Medical University of South Carolina
A. Logan - Early Career Tenure-track Researcher, Medical University of South Carolina
Anindita Nath - Graduate Trainee, University of Texas at El Paso

Duplicate of Hispanic cancer

Despite lower incidence rates of more common cancers, such as breast, colon, lung, and prostate, Hispanics have a disproportionately higher incidence and mortality for cancers associated with infectious agents such as liver, cervical, and gastric cancer (GC). For example, GC…

Scientific Questions Being Studied

Despite lower incidence rates of more common cancers, such as breast, colon, lung, and prostate, Hispanics have a disproportionately higher incidence and mortality for cancers associated with infectious agents such as liver, cervical, and gastric cancer (GC). For example, GC incidence is 1.6 times higher in Hispanics compared to non-Hispanic Whites (NHW) with a nearly two-fold increase in mortality. The increase in mortality in Hispanics is often attributed to poor socioeconomic status and the resultant delayed presentation to health care systems.
To study the variation in the incidence of all cancers in Hispanics based on immigration status.

Project Purpose(s)

Disease Focused Research (cancer)
Population Health

Scientific Approaches

Specific Aim 1: To assess the effect of acculturation on the incidence of Hispanic cancer
Research Plan:
.All of Us research program will be queried to identify participants with gastric cancer. The incidence of gastric cancer among immigrant and non-immigrant Hispanics will be assessed and compared with the non-Hispanic control group. Age, sex, country of birth, immigration status, socioeconomic status, lifestyle factors (smoking, alcohol), and comorbidities will be used in a multivariate logistic regression analysis to identify factors that are associated with gastric cancer in Hispanics. The impact of acculturation on variations in projected incidence will be calculated as described previously.

Specific Aim 2: To assess the incidence of cancer in Hispanic/ Latino ethnic subgroups

Anticipated Findings

We believe the incidence of cancers, particularly for gastric, cervical and liver cancers will be different based on the country of origin and immigration status.
Although, there is significant evidence about increased incidence and mortality of gastric cancer in Hispanics, there is very little to no evidence on the impact of acculturation on cancer incidence. The proposed study will help identify the impact of acculturation on cancer incidence. Additionally, the variations in the incidence of cancer among different Hispanic race/ethnic subgroups residing in United states combined with genomic data will give us an idea about the high-risk groups and the genomic variations that account for variations in incidence. Such comprehensive analysis combining population data with genomic data is currently not available for cancer in Hispanics. Data form this analysis will add new information that could be utilized to improve cancer outcomes in this race/ethnic subgroup.

Demographic Categories of Interest

Research Team

Owner:

Maheswari Senthil - Mid-career Tenured Researcher, University of California, Irvine

Collaborators:

Farideh Dehkordi-Vakil - Other, University of California, Irvine

Lung Cancer Risk

This is an initial exploratory workspace to determine if demographic and clinical factors can predict risk of developing lung cancer.

Scientific Questions Being Studied

This is an initial exploratory workspace to determine if demographic and clinical factors can predict risk of developing lung cancer.

Project Purpose(s)

Educational

Scientific Approaches

We will determine a cohort of subjects with proven lung cancer. Then, identify a cohort matched by age, gender, and ethnicity that has no evidence of lung cancer. We will determine if a combination of demographic and clinical factors can accurately classify subjects into lung cancer +/- groups.

Anticipated Findings

This is an educational effort to learn about the data in All of Us and the tools for exploration. These findings are preliminary for future studies to improve prognosis prediction for cancer.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Adam Alessio - Late Career Tenured Researcher, Michigan State University

Wearables Data and the Human Phenome

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease. Higher physical activity is associated with lower prevalence and better outcomes in virtually every human disease. These analyses…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease. Higher physical activity is associated with lower prevalence and better outcomes in virtually every human disease. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence and progression of coded human diseases. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality).

Anticipated Findings

We expect to find that lower levels of activity are associated with a higher prevalence and more rapid progression of chronic diseases. These data will provide the rationale to link wearables data with electronic health records nationwide as a window into behavioral activity choice as a modifiable risk factor for chronic diseases. We may find substantial variation in activity and disease prevalence/severity by socioeconomic status, which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

Research Team

Owner:

Evan Brittain - Mid-career Tenured Researcher, Vanderbilt University Medical Center
Jeffrey Annis - Other, Vanderbilt University Medical Center

Cancer

We want figure out if we can optimize the existing medical recording as powerful tool for early diagnose of cancer and best treatment option based on the diagnosis time.

Scientific Questions Being Studied

We want figure out if we can optimize the existing medical recording as powerful tool for early diagnose of cancer and best treatment option based on the diagnosis time.

Project Purpose(s)

Disease Focused Research (Thyroid cancer )

Scientific Approaches

Using Machine Learning algorithms, supervised neural networks to predict the best treatment model. Within the existing machine learning models, we are using an artificial neural network to recognize the pattern and train it to learn from supervised training data and outputs to make predictions.

Anticipated Findings

Optimization of treatment options for different stage of cancer diagnosis.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Adam Abiri - Project Personnel, University of California, Los Angeles

Antifungals

The focus of the research is to study the use of antifungals and determine their usage and the outcome of the fungal disease based on the specific drug that was used.

Scientific Questions Being Studied

The focus of the research is to study the use of antifungals and determine their usage and the outcome of the fungal disease based on the specific drug that was used.

Project Purpose(s)

Drug Development

Scientific Approaches

Identify patients with fungal infections, such as aspergillus, cryptococcosu and candida, and analyze their treatment regimes and outcome. Determine any link between the length of treatment and outcome.

Anticipated Findings

This analysis will provide additional support that new antifungals ere urgently needed, as currently available drugs are inefficient to improve the clinical outcome of the disease.

Demographic Categories of Interest

Research Team

Owner:

Maurizio Del Poeta - Late Career Tenured Researcher, Stony Brook University Hospital

Heart Failure and Vulnerable Populations

Racial Disparities in Heart Failure (what are the predictors of heart failure and how do these predictors vary by race)

Scientific Questions Being Studied

Racial Disparities in Heart Failure (what are the predictors of heart failure and how do these predictors vary by race)

Project Purpose(s)

Disease Focused Research (congestive heart failure)

Scientific Approaches

Case-cohort disease of all heart failure patients within the registry
How do Social Determinants of Health affect the outcome of various Heart Failure populations?

Anticipated Findings

The implications of the social determinants of health in various HF population are dire and require focused interventions to
improve overall quality of care. The All of Us Registry is the best data available to determine how well are these patients treated
with "real-world" medical therapy. The demographic profile and predictors of worse outcomes are also noteworthy

Demographic Categories of Interest

Research Team

Owner:

Melvin Echols - Mid-career Tenured Researcher, Morehouse School of Medicine

Collaborators:

Robert Meller - Mid-career Tenured Researcher, Morehouse School of Medicine

Pregnancy heart rate study

Women’s exposure to social adversity over the life course is associated with altered physiologic set points within stress regulatory systems (e.g. autonomic and endocrine systems). Such physiologic alterations render some women more vulnerable to adverse mental, physical and reproductive health…

Scientific Questions Being Studied

Women’s exposure to social adversity over the life course is associated with altered physiologic set points within stress regulatory systems (e.g. autonomic and endocrine systems). Such physiologic alterations render some women more vulnerable to adverse mental, physical and reproductive health trajectories. Circadian heart rate parameters are an emerging pre-morbid biomarker of sympathovagal balance of the autonomic stress response but few studies have studied this in the context of pregnancy. Therefore, the purpose of this study is to 1) describe within-person trajectories of circadian heart rate over the duration of pregnancy, 2) examine whether between-person variation in these parameters is associated with social and intergenerational adversity and 3) whether within- and between-person nocturnal heart rate parameters are associated with physical activity in pregnancy.

Project Purpose(s)

Social / Behavioral
Methods Development

Scientific Approaches

We will curate a subset of data from the National Institutes of Health All of Us Research Program for secondary analysis. We will examine a subset of individuals with 1) a confirmed pregnancy and 2) available Fitbit data. Minute-level data on heart rate from the Fitbit will be used to compute circadian heart rate parameters (e.g. nocturnal dipping ratio). Intensive longitudinal data analysis methods will be used for within-person analyses. Multilevel modeling will be used to examine between-person analyses. This research will enhance understanding of stress-related programming effects on pathophysiologic pregnancy complications.

Anticipated Findings

We hypothesize that pregnancy will be associated with an overall increase in heart rate and changes in the nocturnal dipping ratio over the course of pregnancy. Social and intergenerational adversity will be associated with baseline nocturnal dipping ratio and trajectories of heart rate over gestation.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Crystal Epstein - Early Career Tenure-track Researcher, University of North Carolina, Greensboro

Duplicate of D043 AOU_DEMO_PheRS implementation_v4

Genetic association studies often examine features independently, potentially missing subpopulations with multiple phenotypes that share a single cause. Phenotype risk scores (PheRS) is an approach published by Lisa Bastarache, et al. to help identify patients with unrecognized Mendelian disease patterns…

Scientific Questions Being Studied

Genetic association studies often examine features independently, potentially missing subpopulations with multiple phenotypes that share a single cause. Phenotype risk scores (PheRS) is an approach published by Lisa Bastarache, et al. to help identify patients with unrecognized Mendelian disease patterns using phenotypes from the electronic health record (EHR). Our specific question is to test wether we can replicate PheRS approach for three mendelian diseases including CYSTIC FIBROSIS (CF), HEMOCHROMATOSIS (HH) and SICKLE CELL(SC) ANEMIA in All of Us cohort.

Project Purpose(s)

Methods Development
Other Purpose (This work is a result of an All of Us Research Program Demonstration Project. The projects are efforts by the Program designed to meet the program's goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. This work was reviewed and overseen by the All of Us Research Program Science Committee and the Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use)

Scientific Approaches

PheRS utilizes Mendelian diseases descriptions annotated with Human Phenotype Ontology (HPO) terms, and these terms are mapped to ICD-9/10 codes. We then calculate the prevalence of each term by taking the number of unique individuals and dividing it by the total number of individuals in All of Us EHR cohort. We further use the -1og10 of the prevalence as the weight for each term. The PheRS for a particular disease is calculated by summing up the weights of each term that is present for an individual. In addition, we produce a residualized PheRS (rPheRS) using a linear regression model adjusted for age, sex, race and the number of unique years for which they have billing data in the EHR (ie, PheRS ∼ Age + Sex + Race + uniq_encounter_years). We use a cubic spline with 3 knots for age. The rPheRS is defined as the studentized residual of the PheRS from this model. Wilcoxon rank sum test is used to test the difference for raw PheRS or rPheRS between case and control groups.

Anticipated Findings

We hope we can replicate PheRS algorithm, using three diseases as examples including cystic fibrosis, hereditary hemochromatosis and sickle cell disease. We hope All of Us could facilitate the discovery of pathogenic variants, refine estimates of penetrance across diverse populations, and provide a more nuanced understanding of inheritance patterns in the future.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Chenjie Zeng - Research Fellow, NIH

Social support and psychological well-being during COVID-19

Our primary aims for conducting this research project are: 1. How is perceived social support associated with individuals’ (a) COVID-10 relational impact, (b) general well-being, and (c) resilience during the COVID-19 pandemic? 2. How is social distancing associated with individuals’…

Scientific Questions Being Studied

Our primary aims for conducting this research project are:
1. How is perceived social support associated with individuals’ (a) COVID-10 relational impact, (b) general well-being, and (c) resilience during the COVID-19 pandemic?
2. How is social distancing associated with individuals’ (a) perceived social support and (b) loneliness?
3. Is physical activity a moderator for the associations proposed in aim 1 and 2?
By examining these aims, we hope to better understand the role social support and physical activity play in individuals’ psychology well-being and well-functioning adaptation during the COVID-19 pandemic. The findings will inform future public health guidelines for navigating a public health crisis.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We plan to use the three-wave data collected in the COPE project. Regression analyses and longitudinal analyses will be performed using R.

Anticipated Findings

We anticipate that results will illuminate that social support and physical activity are important factors that could help maintain or enhance individuals’ psychological well-being. During a public health crisis like COVID-19 where interactions with friends and family are substantially circumscribed, it is pivotal to encourage individuals to be creative in maintaining necessary bonds and connections with close others.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Lingzi Zhong - Research Fellow, University of Utah

SARS-CoV-2 infection Project

The proposed study seeks to investigate the associations between SARS-CoV-2 and the development of diabetes. The primary hypothesis is SARS-CoV-2 will be a risk factor for the onset of diabetes. The secondary hypothesis is that SARS-CoV-2 is a predisposing factor…

Scientific Questions Being Studied

The proposed study seeks to investigate the associations between SARS-CoV-2 and the development of diabetes. The primary hypothesis is SARS-CoV-2 will be a risk factor for the onset of diabetes. The secondary hypothesis is that SARS-CoV-2 is a predisposing factor for other chronic diseases. Additionally, the proposed study will explore if SARS-CoV-2 is associated with other hormone/endocrine conditions (e.g., hyperthyroidism), mental health or substance use of conditions (e.g., alcohol use disorder), and cancer (e.g., kidney cancer, lung cancer, pancreatic cancer). Findings have clinical implications for prevention (e.g., vaccines), screenings, and treatments, post SARS-CoV-2 infection. Moreover, the impact of SARS-CoV-2 will disproportionately impact underserved populations whom are at an increased risk for chronic conditions (e.g., diabetes, cardiovascular, pulmonary).

Project Purpose(s)

Disease Focused Research (SARS-CoV-2)

Scientific Approaches

We will explore associations between SARS-CoV-2 infections and the incidence of diabetes with a focus on underserved patient populations. This study will also explore other associations affecting the incidence of SARS-CoV-2 infections and co-morbidities. The analysis will employ exploratory methods of data analysis such as association plots, heatmaps, and descriptive statistics. Following the exploratory analysis, generalized linear models will explore the associations further while controlling for patient characteristics and other factors. Multiplicity corrections will control the incidence of type I errors and ensure replicability of research results. The data will include information on past SARS-CoV-2 infections, type I and II diabetes, demographic characteristics, and other hormone/endocrine conditions, mental health or substance use conditions, and cancer. Data will be stratified to assess the change in risk of these conditions for underserved patient populations.

Anticipated Findings

The primary hypothesis is SARS-CoV-2 will be a risk factor for the onset of diabetes. The secondary hypothesis is that SARS-CoV-2 is a predisposing factor for other chronic diseases. Additionally, the proposed study will explore if SARS-CoV-2 is associated with other hormone/endocrine conditions (e.g., hyperthyroidism), mental health or substance use of conditions (e.g., alcohol use disorder), and cancer (e.g., kidney cancer, lung cancer, pancreatic cancer). Findings have clinical implications for prevention (e.g., vaccines), screenings, and treatments, post SARS-CoV-2 infection.

Demographic Categories of Interest

Research Team

Owner:

Leslie Flaco - Graduate Trainee, University of Texas at El Paso
Denisse Urenda - Graduate Trainee, University of Texas at El Paso
Amy Wagler - Mid-career Tenured Researcher, University of Texas at El Paso

Collaborators:

ATIQUR CHOWDHURY - Graduate Trainee, University of Texas at El Paso
Eric Diaz - Undergraduate Student, University of Texas at El Paso

Learning

This workspace will be used for learning how to use Research Workbench (cohort builder and creating datasets) and exploring data available in AllOfUs.

Scientific Questions Being Studied

This workspace will be used for learning how to use Research Workbench (cohort builder and creating datasets) and exploring data available in AllOfUs.

Project Purpose(s)

Other Purpose (Learning how to use Researcher Workbench and exploring data available in AllOfUs.)

Scientific Approaches

Exploring the data available in AllofUs - Surveys, EHR, Physical measurements and Wearables data. Exploring the number of participants that overlap among different datasets of the program.

Anticipated Findings

Develop a deeper understanding of the data available in AllofUs and how to use the data available to formulate new research hypothesis.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Chinari Pawan Kumar Patro - Senior Researcher, CureScience

Blood Pressure and Race in Health Disparities

My purpose in exploring these data are to understand how cardiovascular activity and social determinants of health differentially predict disease as a function of race.

Scientific Questions Being Studied

My purpose in exploring these data are to understand how cardiovascular activity and social determinants of health differentially predict disease as a function of race.

Project Purpose(s)

Disease Focused Research (Blood Pressure)
Population Health
Social / Behavioral
Drug Development
Methods Development
Ancestry

Scientific Approaches

I plan to use traditional statistical programs to analyze these data, and research methods will be primarily group comparisons and regression (i.e., moderation/mediation) analyses.

Anticipated Findings

I anticipate racial differences in the association between disease and both cardiovascular activity and social determinants of health. Additional differences such as between gender, may also be observed.

Demographic Categories of Interest

Research Team

Owner:

DeWayne Williams - Early Career Tenure-track Researcher, University of California, Irvine

Oculoplastics Project

We aim to describe the incidence of oculoplastics conditions that are reflected in the dataset and compare these values to published data to establish a new baseline.

Scientific Questions Being Studied

We aim to describe the incidence of oculoplastics conditions that are reflected in the dataset and compare these values to published data to establish a new baseline.

Project Purpose(s)

Methods Development

Scientific Approaches

This study will use values from the AoU dataset based on the headings and sub-headings provided. In addition, a literature review will be performed using PubMed.

Anticipated Findings

Given the AoU dataset is larger and more diverse than those medical centers represented in the literature, we hypothesize the prevalence of oculoplastics conditions will vary from the established values. These findings will help validate or else bring into question established values of disease prevalence.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Victoria North - Research Fellow, Columbia University

Cardiovascular Disease

Our research is focused on exploring patients with various cardiovascular diseases and analyze how different medications are being used to treat these diseases within the All of Us dataset. This research will also focus on exploring how rural populations are…

Scientific Questions Being Studied

Our research is focused on exploring patients with various cardiovascular diseases and analyze how different medications are being used to treat these diseases within the All of Us dataset. This research will also focus on exploring how rural populations are treated in comparison to the urban population to determine any differences in the course of treatment and to determine if a patients treatment in an urban vs rural setting can impact a patients healthcare. We are exploring the hypothesize that while similar medications are being utilized, the limited access to healthcare in a rural setting has an increase in disease progression and the need for additional medications.

Project Purpose(s)

Disease Focused Research (cardiovascular system disease)

Scientific Approaches

For this study, we will utilize algorithms in the All of Us data to determine the various medications being prescribed for different cardiovascular diseases and for individuals in urban vs rural populations. We will determine if the medications being used vary based upon a urban vs rural setting for different cardiovascular diseases and how these medications are being used during disease progression.

Anticipated Findings

We anticipate that with the available data sets from the different sites across the country, that similar medications will be utilized in urban and rural setting; however, with the limited access to healthcare in a rural setting has an increase in disease progression and the need for additional medications to help control a patients disease progression. This research will hopefully highlight the importance continued care to slow or limit disease progression.

Demographic Categories of Interest

Research Team

Owner:

Jacob Neumann - Mid-career Tenured Researcher, West Virginia School of Osteopathic Medicine

Covid Mental Health

There is a rapidly growing body of literature--both national and international--reporting increased incidence of mental health conditions due to the COVID-19 pandemic. However, most of the risk and protective factors reported by recent COVID studies are known predictors of major…

Scientific Questions Being Studied

There is a rapidly growing body of literature--both national and international--reporting increased incidence of mental health conditions due to the COVID-19 pandemic. However, most of the risk and protective factors reported by recent COVID studies are known predictors of major psychiatric conditions. Further, most prior studies focus on the association of one risk factor with mental health risk at a time using linear models and do not examine multiple risk factors concurrently. More flexible machine learning (ML) approaches that can automatically detect main and interaction effects may provide novel insights for psychopathology by capturing the hidden nonlinear dependence between risk/protective characteristics.

Project Purpose(s)

Disease Focused Research (disease of mental health)

Scientific Approaches

We plan to leverage interpretable ML models to discover actionable insights into the factors associated with psychological vulnerability and resilience using high-dimensional data including sociodemographic, clinical, survey, and lifestyle features.

Anticipated Findings

We expect that the findings from our study may help discover new insights into predictors of psychological vulnerability and resilience that may be unique to the period of the COVID-19 pandemic.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Heather Lee - Research Fellow, Mass General Brigham

Collaborators:

zhaowen liu - Research Fellow, Mass General Brigham
Karmel Choi - Early Career Tenure-track Researcher, Mass General Brigham

Hypertension

Hypertension in biomedically underserved populations to determine links to inflammatory conditions and heart disease.

Scientific Questions Being Studied

Hypertension in biomedically underserved populations to determine links to inflammatory conditions and heart disease.

Project Purpose(s)

Disease Focused Research (Hypertension)
Population Health

Scientific Approaches

Hypertension in biomedically underserved populations to determine links to inflammatory conditions and heart disease.

Anticipated Findings

Hypertension in biomedically underserved populations to determine links to inflammatory conditions and heart disease.

Demographic Categories of Interest

Research Team

Owner:

Christina Jordan - Early Career Tenure-track Researcher, University of Mississippi Medical Center

State-level Activity Inequality

How is physical activity distributed within states in the US? Analysis of such activity distributions and inequality can reveal important relationships between physical activity disparities, health outcomes, and modifiable factors, as studied by Althoff et al. in their paper Large-scale…

Scientific Questions Being Studied

How is physical activity distributed within states in the US? Analysis of such activity distributions and inequality can reveal important relationships between physical activity disparities, health outcomes, and modifiable factors, as studied by Althoff et al. in their paper Large-scale physical activity data reveal worldwide activity inequality (2017).

Project Purpose(s)

Educational

Scientific Approaches

The cohort will consist of Fitbit users in the US, with analysis being subdivided to the state level. Various graphs will be utilized to help visualize the low- and high-activity trends across states. Well-defined measures such as the Gini coefficient will be used to aid in the analysis of activity inequality.

Anticipated Findings

The study aims to find relationships between activity inequality and health outcomes, such as obesity levels. With the growing accessibility of fitness trackers and activity sensors built into personal devices, this study hopes to leverage the volume of available data and potentially inform measures to improve population activity and health.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Juseong Kim - Undergraduate Student, Duke University

Collaborators:

Geetika Singh - Graduate Trainee, Duke University

Effects of COVID-19 on Socialization and Isolation in Latino Populations

The primary purpose is to investigate the relationships between 1) individual characteristics (e.g., age), 2) socialization, 3) loneliness/isolation, and 4) health. Emphasis will be given to comparing aging Latino/Hispanic populations to non-Latino/Hispanic populations and contextualizing results within COVID-19. To emphasize…

Scientific Questions Being Studied

The primary purpose is to investigate the relationships between 1) individual characteristics (e.g., age), 2) socialization, 3) loneliness/isolation, and 4) health. Emphasis will be given to comparing aging Latino/Hispanic populations to non-Latino/Hispanic populations and contextualizing results within COVID-19. To emphasize aging, we plan to focus on adults aged 50+. The data may inform on the experience of diverse aging populations during isolating events. Results may further understanding of the effects of isolation on activity, socialization, and health in aging populations across cohorts. These data will help inform on the relationships between socialization and loneliness in aging adults. This study will investigate relationships between (dis)engagement and health; health may be a barrier to traditional forms of socialization, while isolation may similarly reduce health outcomes. Findings will be contextualized within ethnicity to contribute to research on ethnic minorities.

Project Purpose(s)

Social / Behavioral
Other Purpose (Findings from this study may contribute to manuscripts for scientific journals and/or conference submissions.)

Scientific Approaches

Dataset development will utilize All of Us data and will occur in the researcher workbench. Data will be pulled from several All of Us datasets including: The Basics, Overall Health, Lifestyle, Personal Medical History, Health Care Access & Utilization, COVID-19 Participant Experience (COPE), and Physical Measurements. All data analysis and data visualization will be conducted within the All of Us workbench. Normality tests, regression analyses and tests of correlation (e.g. chi-square analysis, Pearson correlation, etc.) will be utilized in analyses. Results will be reported in APA style.

Anticipated Findings

The objectives of this study are to inform on the relationships between individual characteristics, socialization, loneliness/isolation, and health, particularly in aging Latino adults in the context of COVID-19. We hypothesize variables associated with disadvantage & poorer health will negatively affect downstream outcomes, including socialization; persons with less disadvantage and greater access may have higher levels of socialization; this may reduce isolation/loneliness. This may also relate to better health outcomes. However, we may uncover contributing evidence of the benefit of large/multigenerational homes, which are common in Latino cultures as they help buffer isolation for at-risk populations. This contributes to minority & marginalized peoples research by providing greater context for health/behavioral research. It may also inform future meaningful interventions to improve health and reduce isolation across diverse aging populations, particularly during isolating events.

Demographic Categories of Interest

Research Team

Owner:

Sarah Hubner - Graduate Trainee, University of Nebraska, Omaha
Julie Blaskewicz Boron - Mid-career Tenured Researcher, University of Nebraska, Omaha

Collaborators:

Athena Ramos - Senior Researcher, University of Nebraska Medical Center

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