Research Projects Directory

Research Projects Directory

Information about each research project within the Workbench is available in the Research Projects Directory below. Approved researchers provide their project’s research purpose, description, populations of interest and more. This information helps All of Us ensure transparency on the type of research being conducted.

At this time, all listed projects are using data in the Registered Tier. The Registered Tier contains individual-level data from electronic health records, survey answers, and physical measurements. These data have been altered to protect participant privacy.

Note: Researcher Workbench users provide information about their research projects independently. Any views expressed in the Research Projects Directory belong to the relevant users and do not necessarily represent those of the All of Us Research Program.

Information in the Research Projects Directory is also cross-posted on AllofUs.nih.gov in compliance with the 21st Century Cures Act.

There are currently 291 active workspaces. This information was updated on 12/5/2020.

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Mast cell disorders, depression, and inflammation

Project Purpose(s)

  • Disease Focused Research (mast cell disorders and depression)
  • Social / Behavioral ...

Scientific Questions Being Studied

With few exceptions (e.g., Nicoloro, Lobel & Wolfe, 2016), mast cell disorders have received little attention from psychological science. Therefore, reliable estimates of the prevalence of emotional distress in this population are largely nonexistent. Documenting levels, types, and contributors to depression in this population can facilitate the development of appropriate interventions and highlight pathways through which emotional states such as depression may exacerbate mast cell disorders, as hypothesized by some researchers (Theoharides & Konstantinidou, 2007). There is considerable evidence in other populations that negative emotional states can influence physical health through a variety of pathways; a number of these are implicated in mast cell disorders, including the immune, endocrine, cardiovascular, and central nervous systems(e.g., Kiecolt-Glaser, McGuire, Robles, & Glaser, 2002; Herbert & Cohen, 1993; Kiecolt-Glaser, Malarkey, Cacioppo, & Glaser, 1994).

Scientific Approaches

We plan to examine the relationship between depression and markers of inflammation in individuals with mast cell disorders.

Anticipated Findings

The findings can be used to identify individuals at risk, to develop effective interventions, to inform the care of people with mast cell disorders,
and to reduce their suffering.

Demographic Categories of Interest

  • Others

Research Team

Owner:

  • Jennifer SantaBarbara - Research Fellow, University of California, Los Angeles

MDD_test

Project Purpose(s)

  • Population Health ...

Scientific Questions Being Studied

Initial exploratory data analysis to assess the AoU data potential hypotheses that could be explored

Scientific Approaches

Case-Control statistical analysis to compare across significant differences across various sub-cohorts in AoU based on labels that will be derived based on survey outcomes.

Anticipated Findings

To help develop better understanding of AoU datasets and workbench that will be used to inform future research studies

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth
  • Gender Identity
  • Sexual Orientation
  • Geography
  • Disability Status
  • Access to Care
  • Education Level
  • Income Level

Research Team

Owner:

  • Abhishek Pratap - Senior Researcher, Sage Bionetworks

Mental Health and Substance Use Demo Projects

Project Purpose(s)

  • Disease Focused Research (disease of mental health)
  • Population Health ...

Scientific Questions Being Studied

What are the prevalences of mental health conditions in the AoURP?

Scientific Approaches

Not available.

Anticipated Findings

AoURP data can be used to assess mental health conditions in previously under-represented populations.

Demographic Categories of Interest

  • Sex at Birth
  • Education Level
  • Income Level

Research Team

Owner:

  • Chen Yeh - Project Personnel, Northwestern University

Collaborators:

  • Kai Yin Ho - Project Personnel, Northwestern University
  • Joyce Ho - Mid-career Tenured Researcher, Northwestern University

Mental Health Demonstration Project

Project Purpose(s)

  • Disease Focused Research (generalized anxiety disorder, depressive disorder, bipolar disorder)
  • 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 Questions Being Studied

As a demonstration project, this study aimed to explore the usability of the All of Us dataset and examined the prevalence of mental health conditions in the All of Us Research Program cohort. Specifically, we explored the lifetime prevalence of depressive disorder, bipolar disorder, and generalized anxiety disorder.

Our study looked prevalence rates for the above conditions in the following ways:
1. Prevalence in EHR data available by various demographic factors
2. Cohort characteristics
3. Congruency for diagnoses in EHR and self-report questionnaire
4. Among individuals who self-report as having been diagnosed with a mental health condition listed above, the percentage of individuals in treatment and associations between treatment and various demographic factors

Scientific Approaches

In this analysis, we calculated prevalence of mental health conditions by leveraging demographic information, questionnaire responses, and EHR data Specifically, we utilized the following surveys: Basics, Overall Health, Personal Medical History, and Healthcare Access PPIs. We utilized EHR data by creating a cohort of individuals with specific diagnoses code in their EHR. We referenced all relevant parent and child SNOMED codes for each mental health condition of the investigation (documented in Concept Set). Associations were calculated using Chi Square.

Anticipated Findings

We anticipated that the prevalence rates found in All of Us will be consistent with previous large scale studies, such as the National Comorbidity Survey. We found that the All of Us dataset is sensitive to detecting mood disorders and is usable for examining mental health conditions.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Chen Yeh - Project Personnel, Northwestern University

Collaborators:

  • Kai Yin Ho - Project Personnel, Northwestern University
  • Joyce Ho - Mid-career Tenured Researcher, Northwestern University

miRNA research

Project Purpose(s)

  • Population Health
  • Ancestry ...

Scientific Questions Being Studied

Understanding genetic variation in the human genome. Genetic variation i s what makes us all unique, and at the same time, it is the biological basis of many disorders and disease disparities. The continued efforts to sequence individuals have revealed an unprecedented number of genetic variations.
Most of these variants will have a null or negligible effect. However, hidden in among them there will be those with important physiological and medical consequences. My objective is to combine bioinformatic approaches with high-throughput methods to interrogate the mechanistic impact of population variants affecting miRNA function. This will provide the field a new perspective for their interpretation.

Scientific Approaches

Identify generic variants with a potential impact on RNA structure, base-pairing or thermodynamics.
Identify which tissues the miRNA will be expressed and therefore propose hypothesis driven diseases that might be of relevance.
Study a cohort of individuals with and without the genetic variants.

Anticipated Findings

Create a framework to understand the role of genetic variants on non-coding RNAs.
Establish association between variants in microRNAs and diseases.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Xavier Bofill De Ros - Research Fellow, NIH

Miscellaneous

Project Purpose(s)

  • Other Purpose (Trouble-shooting, thanks for your help, Francis.) ...

Scientific Questions Being Studied

Trouble-shooting, thanks for your help, Francis.

Scientific Approaches

no approach is necessary for this workspace because this is for operational use only.

Anticipated Findings

Trouble-shooting, thanks for your help, Francis.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Guohai Zhou - Early Career Tenure-track Researcher, Massachusetts General Hospital

Collaborators:

  • Robert Carroll - Other, All of Us Program Operational Use
  • Francis Ratsimbazafy - Other, All of Us Program Operational Use

NIMIWAE

Project Purpose(s)

  • Methods Development ...

Scientific Questions Being Studied

How can we use deep learning techniques to handle EHR data with missingness that is non-ignorable (or MNAR)?
Does a method that correctly accounts for MNAR missingness improve performance of imputation of missing data? And does this improvement translate to improvement in downstream tasks like prediction of mortality or disease outcome. Would such a method aid clinicians in risk assessment, and guide decision for early intervention?

Scientific Approaches

We plan to look at real life EHR datasets, and either simulate missingness on fully-observed EHR data, or attempt to validate our method via prediction of some outcome of interest using the dataset with inherent missingness. We will use the imputed dataset to perform this learning task, in an indirect way to validate the quality of the imputation.

Anticipated Findings

We anticipate that properly accounting for the MNAR nature of EHR data will increase performance of the imputation of missing data, thereby increasing performance of prediction of disease or mortality. We believe that this would help guide treatment decisions, and help with risk assessment for patients.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • David Lim - Graduate Trainee, University of North Carolina, Chapel Hill

NIV Failure Characterization

Project Purpose(s)

  • Disease Focused Research (respiratory failure) ...

Scientific Questions Being Studied

We are attempting to identify the failure mechanisms of noninvasive ventilation therapy in patients with acute respiratory failure.

Scientific Approaches

We plan to validate an existing phenotyping method for ventilation therapy patients and generate summary statistics between the different cohorts to characterize any major differences.

Anticipated Findings

We anticipate that our approach will validate the previously created phenotyping algorithm and identify major differences between patients that successfully undergo noninvasive ventilation therapy and those that fail noninvasive therapy and require subsequent endotracheal intubation.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Patrick Essay - Graduate Trainee, University of Arizona

Collaborators:

  • Vignesh Subbian - Early Career Tenure-track Researcher, University of Arizona

NW_AOU_Informatics

Project Purpose(s)

  • Population Health ...

Scientific Questions Being Studied

We are informatics researchers using AOU data to study research data sets. We want to understand the kinds of data enclosed and the utility for data reuse research.

Scientific Approaches

Data mining, mostly.

Anticipated Findings

We will find the data reuse potential of AOU.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Nick Williams - Research Associate, NIH

Obesity analysis

Project Purpose(s)

  • Disease Focused Research (obesity) ...

Scientific Questions Being Studied

state level obesity

Scientific Approaches

Not available.

Anticipated Findings

state level obesity disparities after adjusting for socioeconomic factors

Demographic Categories of Interest

  • Sex at Birth
  • Education Level
  • Income Level

Research Team

Owner:

  • Guohai Zhou - Early Career Tenure-track Researcher, Massachusetts General Hospital

Collaborators:

  • Francis Ratsimbazafy - Other, All of Us Program Operational Use
  • Paulette Chandler - Early Career Tenure-track Researcher, Massachusetts General Hospital
  • Andrea Ramirez - Other, All of Us Program Operational Use
  • Elizabeth Karlson - Late Career Tenured Researcher, Massachusetts General Hospital
  • Cheryl Clark

obesitypaper02202020

Project Purpose(s)

  • Disease Focused Research (obesity) ...

Scientific Questions Being Studied

how does bmi differ by state, ses, race/ethnicity

Scientific Approaches

Not available.

Anticipated Findings

obesity will vary by region

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Paulette Chandler - Early Career Tenure-track Researcher, Massachusetts General Hospital

Older Adults

Project Purpose(s)

  • Population Health
  • Methods Development ...

Scientific Questions Being Studied

Exploration of traditional regression models vs. machine learning methods for large population health studies of older adults (65+).

Scientific Approaches

Application and comparison of traditional/contemporary regression models (e.g. generalized linear models) and "machine learning" methods (e.g. gradient boosting, random forest, LASSO) in supervised learning applications.

Anticipated Findings

There is an active debate in the literature as to whether (potentially) increased predictive ability of machine learning techniques is worth the additional, potential black-box complexity vs. traditional regression models. A particular thread of this discussion examines the potential for unintended bias in machine learning methods.

Demographic Categories of Interest

  • Age

Research Team

Owner:

  • John Boscardin - Other, University of California, San Francisco

OMOP

Project Purpose(s)

  • Methods Development ...

Scientific Questions Being Studied

I am interested in studying clinical care across populations. How well can we predict adverse outcomes in large-scale datasets? How does this differ across populations and across diseases?

Scientific Approaches

We will use supervised learning methods to generate time series data to predict adverse outcomes. We will use standard baseline models like logistic regression and support vector machines and then more state-of-the-art methods including convolutional neural networks.

Anticipated Findings

I hope to determine how accurately can machine learning models predict adverse outcomes across different populations.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Irene Chen - Graduate Trainee, Massachusetts Institute of Technology

One

Project Purpose(s)

  • Ethical, Legal, and Social Implications (ELSI) ...

Scientific Questions Being Studied

I am interested in exploring the race and ethnicity distribution of AoU participants. It is important that AoU participants represent the diversity of the United States.

Scientific Approaches

We will use basic statistical methods to compare race/ethnicity and socioeconomic status of AoU participants of different US regions.

Anticipated Findings

By understanding trends in research participation, we can develop ways to promote a more equitable distribution of the risks and benefits of health research

Demographic Categories of Interest

  • Race / Ethnicity
  • Income Level

Research Team

Owner:

  • Susan Passmore - Other, University of Wisconsin, Madison

One

Project Purpose(s)

  • Ethical, Legal, and Social Implications (ELSI) ...

Scientific Questions Being Studied

I am interested in exploring the race and ethnicity distribution of AoU participants. It is important that AoU participants represent the diversity of the United States.

Scientific Approaches

We will use basic statistical methods to compare race/ethnicity and socioeconomic status of AoU participants of different US regions.

Anticipated Findings

By understanding trends in research participation, we can develop ways to promote a more equitable distribution of the risks and benefits of health research

Demographic Categories of Interest

  • Race / Ethnicity
  • Income Level

Research Team

Owner:

  • Susan Passmore - Other, University of Wisconsin, Madison

ophthalmology epidemiology

Project Purpose(s)

  • Disease Focused Research (eye diseases)
  • Population Health ...

Scientific Questions Being Studied

We would like to evaluate the epidemiology, treatments, and health outcomes of eye diseases using the diverse population in the All Of Us project. Over 12 million people in the United States over the age of 40 have visual impairment, and over 3 million have visual impairment despite glasses, contacts, or other treatments. Visual impairment has severe impacts on patients' quality of life and mortality. There are many common causes of visual impairment, including some reversible (such as cataract) and others that are treatable but can still cause irreversible vision loss (macular degeneration, glaucoma, diabetic retinopathy). Some of these diseases disproportionately impact minority populations (e.g. glaucoma in African Americans and Hispanics).
We hope to broadly characterize the prevalence of eye diseases in this cohort, as well as associated medical and surgical treatments. We hope to be able to investigate risk factors, patterns and outcomes of treatment of different eye diseases.

Scientific Approaches

We plan to primarily use the EHR, survey, and physical measurements dataset to describe the epidemiology of eye diseases, using encounter-level billing codes to determine their presence or absence. We plan to investigate risk factors for these eye diseases, including demographics, medications, physical measurements (to the extent available), survey data, and other associated diagnoses. We will begin with simple descriptive statistics. In diagnoses with sufficiently sized cohort, we will also build logistic regressions to evaluate risk factors for diagnosis.
We will also evaluate treatment patterns (medical and surgical) for different eye diseases, using EHR data of medications and surgeries undergone. We will characterize demographic and patterns in patterns of medications and surgeries.

Anticipated Findings

We anticipate that our findings will contribute broadly to the knowledge of epidemiology of eye diseases in the US, as well as improve our understanding of patterns of treatments and outcomes of eye diseases in the US. In this diverse population, we will also be able to see if there are disparities in eye diseases and their treatment patterns and outcomes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Sophia Wang - Early Career Tenure-track Researcher, Stanford University

Opioid Use

Project Purpose(s)

  • Disease Focused Research (Back Pain and Opioid Use) ...

Scientific Questions Being Studied

We will look at Opioid Use and Back Pain.

We will look to see if opioid use can be reduced with specific interventions, and who is most at risk for being prescribed opioids.

Scientific Approaches

We will use the available cohorts within the All of Us study group to analyze the risk factors for back pain and opioid use.

Anticipated Findings

We hope to find groups at risk for opioid use or back pain and intervene earlier.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth
  • Gender Identity
  • Sexual Orientation
  • Geography
  • Disability Status
  • Access to Care
  • Education Level
  • Income Level

Research Team

Owner:

  • Phillip Cezayirli - Research Fellow, Albert Einstein College of Medicine

Opioid Use in Cancer Patients

Project Purpose(s)

  • Disease Focused Research (cancer)
  • Population Health ...

Scientific Questions Being Studied

Is there a difference between how much patients with cancer use opioids depending on ethnicity and environmental setting

Scientific Approaches

Not available.

Anticipated Findings

That there is a racial disparity among opioid use as evidenced by how much they are prescribed.

Demographic Categories of Interest

  • Sex at Birth
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Research Team

Owner:

  • Toluwalase (Lasė) Ajayi - Early Career Tenure-track Researcher, Scripps Research

Collaborators:

  • Francis Ratsimbazafy - Other, All of Us Program Operational Use

Orgs_NCD

Project Purpose(s)

  • Methods Development ...

Scientific Questions Being Studied

A noncommunicable disease (NCD) is traditionally thought of as a disease that is not spread from human to human, such as heart disease or cancer. Over the past few decades however, researchers have found pathogenic organisms (POs) that either increase risk for or directly cause what would be traditionally thought of as an NCD, such as certain human papillomavirus strains causing cervical cancer or Epstein-Barr virus increasing the risk of developing multiple sclerosis. In this study we are exploring if there are still unknown associations between POs and human disease. Previously, utilizing a different cohort, we identified many new associations. However, we would like to verify these results by seeing which associations replicate on the All of Us data. Finding these relationships between POs and human disease would highlight the importance of vaccination, as preventing the infection could also protect or reduce a person’s risk of developing other diseases later in life.

Scientific Approaches

We plan to test models developed using a different cohort on the All of Us data.
Datasets:
- Clinical diagnoses for many different diseases
- Laboratory measurements of antibody titers for different pathogenic organisms
- Sociodemographic data to adjust for possible confounding.

Research Methods:
We will be using statistical analysis to look for associations between pathogenic organisms and disease diagnoses. Our previously built model uses a logistic regression to calculate this association.
Tools:
For the most part we will be using custom R and Python code.

Anticipated Findings

If we can replicate our results found in the previously analyzed cohort, we will have identified new links between certain pathogenic organisms and human diseases. Previously, similar results describing how certain human papillomavirus strains cause most cervical cancers helped spur on the development of a vaccine against those strains and after widescale rollout of that vaccine we are starting to see significant drops in the rate of cervical precancers. We would hope our results could encourage similar developments.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Mike Lape - Research Assistant, Cincinnati Children's Hospital Medical Center

OSA and co-morbidities

Project Purpose(s)

  • Disease Focused Research (obstructive sleep apnea) ...

Scientific Questions Being Studied

We want to look at the relationship between obstructive sleep apnea (OSA) and co-morbidities. The high morbidity and mortality in OSA is linked with cardiovascular disease in particular, but conditions like hypertension are very difficult to treat in OSA. Understanding the relationships and timeline of development will help us look for underlying mechanisms and point to ways to address health issue in OSA beyond just the breathing problem per se. For example, our group has found links between stress as reflected in physiology (blood repssure, diabetic status) and mental status (stress, depression, anxiety). Our work has also shown that sex differences as so substantial as to mean OSA populations should be studied separately in men and women.

Scientific Approaches

The core of the data set will be people diagnosed with OSA. There are three main approaches. 1) describe point-in-time relationships at time of OSA diagnosis with markers of physiology and mental health. 2) Describe within-group development over time of comorbidities including diagnoses of cardiovascular conditions (e.g., hypertension) and symptoms, looking at the time between diagnosis and increase (if any) in symptomatology. 3) Assess comorbidities in matched groups of people who do not develop OSA, such as people who develop hypertension but not OSA, and studie the effects in 1) and 2) between OSA and non-OSA groups.

Anticipated Findings

The findings will help elucidate to what extent OSA is more than just a breathing problem. More importantly, the timelines will help understand potential mechanisms as they can to a general degree put potential causes before potential effects. We know at present we are missing something about how to treat OSA patients, as standard of care typically does not resolve comorbid symptoms. The proposed study findings will guide is as to what other treatment targets to consider, beyond JUST fixing breathing during sleep (typically with continuous positive airway pressure, or CPAP).

Demographic Categories of Interest

  • Sex at Birth

Research Team

Owner:

  • Paul Macey - Mid-career Tenured Researcher, University of California, Los Angeles