Research Projects Directory

D015-housing (2023)

What is the prevalence of housing insecurity among current participants in the All of Us study? What individual-level factors are related to housing insecurity, including demographics, indicators of health care access, and perceived health status?

Scientific Questions Being Studied

What is the prevalence of housing insecurity among current participants in the All of Us study? What individual-level factors are related to housing insecurity, including demographics, indicators of health care access, and perceived health status?

Project Purpose(s)

Population Health

Scientific Approaches

We will determine the prevalence of housing insecurity in the All of Us study sample using data collected in the Basics module (“worried or concerned about not having a place to live”). We will use housing insecurity as the dependent variable in a multivariate analysis to determine the relationship of healthcare access and health services utilization. Finally, we will report the independent relationship between housing insecurity and healthcare access, adjusting for the covariates and conducting stratified analyses as appropriate.

Anticipated Findings

Recently, investigators examined the relationship of housing insecurity using the 2011-2015 BRFSS and found a 12.6% prevalence among the >228,000 in the study sample. All of Us can replicate these findings among its core participants using questionnaire items similar to those used by investigators.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Hsueh-Han Yeh - Research Associate, Henry Ford Health System
Lisiyu Ma - Graduate Trainee, Henry Ford Health System

Wearable devices and IBS

There is sex differences in wearable devices parameters among patients with Irritable Bowel Syndrome (IBS).

Scientific Questions Being Studied

There is sex differences in wearable devices parameters among patients with Irritable Bowel Syndrome (IBS).

Project Purpose(s)

Disease Focused Research (irritable bowel syndrome)

Scientific Approaches

A cross-sectional study of adult participants from All of US database. All participants with a diagnosis of IBS and used wearable devices (Fitbit) will be included.

Anticipated Findings

The sleep pattern, pulse, steps, nutrition and water intake are different between males and females. These findings will help us better understand the impact of IBS on sleep and lifestyle.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Wafa Aldhaleei - Research Fellow, Mayo Clinic

West Nile Virus

My group and I will be researching the West Nile Virus with the specific aim of identifying the genetic variance in those most susceptible to targeting by mosquito vectors. We will focus on the question of why certain individuals are…

Scientific Questions Being Studied

My group and I will be researching the West Nile Virus with the specific aim of identifying the genetic variance in those most susceptible to targeting by mosquito vectors. We will focus on the question of why certain individuals are more likely to be targeted by mosquitos and, subsequently, more likely to contract the West Nile Virus.

Project Purpose(s)

Disease Focused Research (West Nile Virus)
Educational

Scientific Approaches

My group and I will analyze quantitative data to identify demographic differences in those affected.by West Nile Virus and disproportionately targeted by mosquito vectors.

Anticipated Findings

We anticipate that our findings will help primarily to inform the public, specifically, so that those statistically more susceptible to targeting by mosquitos can take proper precautions.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Deserae O'Dell - Undergraduate Student, Arizona State University

Lung cancer

What kind of lung cancer affects most men and women? Is immunotherapy more successful compared to other treatments?

Scientific Questions Being Studied

What kind of lung cancer affects most men and women? Is immunotherapy more successful compared to other treatments?

Project Purpose(s)

Disease Focused Research (lung cancer)
Population Health
Educational
Drug Development
Methods Development
Control Set

Scientific Approaches

The plan is to use datasets to show how this cancer can affect both men and women. I will also investigate the numbers of people who exposed themselves to immunotherapy. I will compile the data to demonstrate the effectiveness of immunotherapy.

Anticipated Findings

I anticipate that immunotherapy is a better option when it comes to treating lung cancer. The findings will help encourage people to try this type of treatment in comparison to other options.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Cynthia Gonzalez - Undergraduate Student, Arizona State University

Duplicate of v7 controls for genotype_sequence and endocrine disorders_traits_ES

We are interested in identifying the genetic contributors to many different diseases and related quantitative traits. These include diseases such as obesity and its complications, hypermobile Ehlers Danlos syndrome, (hEDS) diabetic kidney disease, and disorders of growth and development, including…

Scientific Questions Being Studied

We are interested in identifying the genetic contributors to many different diseases and related quantitative traits. These include diseases such as obesity and its complications, hypermobile Ehlers Danlos syndrome, (hEDS) diabetic kidney disease, and disorders of growth and development, including differences of sex development (DSDs), as well as quantitative traits such as height, measures of obesity, other endocrine disorders, and metabolite levels. Many of these diseases are either major causes of illness and death, or have no good treatments. Understanding the genetic basis of these diseases and disease-related traits would give clues to new therapeutic approaches. Many of the quantitative traits are known or potential biomarkers for these diseases or potentially even biomarkers of their root causes. Other traits, such as height, serve as highly instructive model quantitative traits that teach us about how genetic factors influence human disease.

Project Purpose(s)

Disease Focused Research (endocrine disorders, hEDS, DSDs, anthropometric and metabolic traits)

Scientific Approaches

We will have genotype or sequence data from cases and will compare with controls from AllofUs with similar genetic ancestry, matched on principal components of ancestry. We will perform association analyses, including individual variants and, for rare variants, in aggregate (e.g. aggregating for a given gene all rare loss of function variants, or all constrained noncoding variants in predicted regulatory regions). We will also use the controls to generate distributions of polygenic risk scores to compare with cases, to test whether common genetic variation influences the penetrance or severity of mutations in known genes, or whether polygenic risk scores themselves contribute to the risk of rare diseases. We will use AllofUs to improve polygenic risk scores for individuals with non-European ancestry, for those diseases and traits where phenotype data are available in AllofUs. Finally, we will perform genetic association analyses for quantitative traits and common endocrine disorders.

Anticipated Findings

These studies will enable rare variant and common variant studies of multiple diseases and quantitative traits, and have the potential to help define the genetic bases of these diseases and traits. In addition, these studies will help shed light on the interplay between common and rare variation in human diseases and quantitative traits. The ancestral diversity in AllofUs will also help extend these findings to individuals with non-European ancestry, reducing the disparities of the uneven application of genetic discoveries.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Joel Hirschhorn - Senior Researcher, Boston Children's Hospital

Collaborators:

Evan Schafer - Project Personnel, Boston Children's Hospital

Cancer Misdiagnosis in EHR

We hope to repurpose variant detection algorithms from genomics to characterize and quantify the impact of cancer misdiagnosis in electronic health records from the All of Us Research Program. Through these algorithms, we hope to compile a phenotype for cancer…

Scientific Questions Being Studied

We hope to repurpose variant detection algorithms from genomics to characterize and quantify the impact of cancer misdiagnosis in electronic health records from the All of Us Research Program. Through these algorithms, we hope to compile a phenotype for cancer through electronic health records that can identify cancer diagnoses earlier. This project has enormous potential for clinical applications such as clinical trials, hospital and patient surveillance, improving cancer survival rates, and finding precise clinical precursors to cancer without using black-box algorithms.

Project Purpose(s)

Disease Focused Research (cancer)

Scientific Approaches

We aim to merge genomic alignment algorithms with electronic health records (EHR) to predict cancer misdiagnosis in the All of Us Research Program. We will identify optimal similarity metrics to compare patients’ insurance billing codes, prescriptions, timing, and other clinical and demographic data. We will use post-cancer diagnosis events, i.e., billing codes, drugs, and lab tests, to improve and evaluate our similarity algorithms. We plan to include data from all participants in the All of Us datasets that have electronic health records relating to cancer.

Anticipated Findings

We anticipate that insertion-like and deletion-like events are tractable within electronic health records. Furthermore, we anticipate these events will vary drastically between genetic ancestry and socioeconomic demographics. These findings would provide the research community with fresh ideas on how to assess patient similarity using electronic health records and how these similarities and differences vary across many demographics found in the United States. In addition to this, we anticipate that the resulting algorithms could be expanded to predict misdiagnosis phenotypes in other diseases.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Camille Krieger - Undergraduate Student, Brigham Young University

Collaborators:

Ethan Bang - Undergraduate Student, Brigham Young University
Matthew Bailey - Early Career Tenure-track Researcher, Brigham Young University
Brian Kim - Undergraduate Student, Brigham Young University

Chlamydia Research Project

The chosen research focus of the group is Chlamydia, a prevalent but often overlooked sexually transmitted infection (STI). Personal experiences and encounters with affected patients have kindled an urgency to explore Chlamydia's impacts. The group is also motivated by the…

Scientific Questions Being Studied

The chosen research focus of the group is Chlamydia, a prevalent but often overlooked sexually transmitted infection (STI). Personal experiences and encounters with affected patients have kindled an urgency to explore Chlamydia's impacts. The group is also motivated by the desire to raise awareness about this common STI, which frequently goes unnoticed. The research's central inquiry revolves around uncovering the potential adverse pregnancy outcomes linked to untreated Chlamydia infections. The team aims to shed light on the long-term implications of these outcomes on both maternal and fetal health. This question is particularly poignant given the frequency of Chlamydia infections and the general lack of awareness surrounding their consequences. Through this collaborative effort, we aspire to contribute significantly to the existing body of knowledge, ultimately fostering better understanding and informed decisions regarding Chlamydia and its effects on maternal and fetal well-being.

Project Purpose(s)

Disease Focused Research (Chlamydia)

Scientific Approaches

-Dataset Selection: This could include medical records, health databases, and potentially survey data.

-Data Analysis: conducting various types of analyses, such as regression analysis to assess associations and potential confounders.

-Comparative Studies: Compare pregnancy outcomes between groups of individuals with untreated Chlamydia infections and those without the infections. Identifying matched control groups or using propensity score matching to control for potential biases.

-Data Visualization: Graphs, charts, and plots can help communicate complex relationships between Chlamydia infections and pregnancy outcomes.

Anticipated Findings

Our project's anticipated findings could provide critical insights into the long-term consequences of untreated Chlamydia infections during pregnancy, leading to advancements in clinical practice, public health, policy decisions, research, and patient education. By shedding light on the potential impacts of Chlamydia on maternal and fetal health, our research could play a crucial role in improving overall reproductive health outcomes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Azucena Mendoza - Undergraduate Student, Arizona State University

Collaborators:

Daniel Trageser - Undergraduate Student, Arizona State University
Christine Roseman - Undergraduate Student, Arizona State University
Amber Hernandez - Undergraduate Student, Arizona State University
Andwelle Foster - Undergraduate Student, Arizona State University

Cannabis and Opioid Use

We aim to examine the prevalence of cannabis and opioid use and disorder in this representative sample and explore the extent to which prevalence rates vary as a function of demographic variables and social determinants of health

Scientific Questions Being Studied

We aim to examine the prevalence of cannabis and opioid use and disorder in this representative sample and explore the extent to which prevalence rates vary as a function of demographic variables and social determinants of health

Project Purpose(s)

Disease Focused Research (Cannabis and opioid use)

Scientific Approaches

We will create a cohort of cannabis and opioid users and test the explore the demographics and social variables within this group.

Anticipated Findings

Better characterization of the person-centered and environmental variables relevant to cannabis and opioid users

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Holly Poore - Research Fellow, Rutgers, The State University of New Jersey

Duplicate of Scarlet R

1) the identifying markers of Crohn's disease and how its diagnosed 2) treatments for Crohn's disease if any that cures a person.

Scientific Questions Being Studied

1) the identifying markers of Crohn's disease and how its diagnosed
2) treatments for Crohn's disease if any that cures a person.

Project Purpose(s)

Disease Focused Research (Crohn's Diease)
Educational
Drug Development
Methods Development
Ancestry
Other Purpose (to learn the indications of Crohn's Disease and is it curable..)

Scientific Approaches

find markers that indicate the illness and then identify the cures/ treatments for the illness.

Anticipated Findings

to find the markings of Crohn's disease and not be misdiagnosed, then to see if there is a treatment or cure for the disease.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

scarlet roberts - Undergraduate Student, Arizona State University

Scarlet R

1) the identifying markers of Crohn's disease and how its diagnosed 2) treatments for Crohn's disease if any that cures a person.

Scientific Questions Being Studied

1) the identifying markers of Crohn's disease and how its diagnosed
2) treatments for Crohn's disease if any that cures a person.

Project Purpose(s)

Disease Focused Research (Crohn's Diease)
Educational
Drug Development
Methods Development
Ancestry
Other Purpose (to learn the indications of Crohn's Disease and is it curable..)

Scientific Approaches

find markers that indicate the illness and then identify the cures/ treatments for the illness.

Anticipated Findings

to find the markings of Crohn's disease and not be misdiagnosed, then to see if there is a treatment or cure for the disease.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

scarlet roberts - Undergraduate Student, Arizona State University

LGBTQIA+ individuals with Cancer and Discrimination

QOL, social support, experiences of discrimination differences among gender identity/sexual orientation in LGBTQIA+ individuals comparing sex organ cancers to non-sex organ cancers

Scientific Questions Being Studied

QOL, social support, experiences of discrimination differences among gender identity/sexual orientation in LGBTQIA+ individuals comparing sex organ cancers to non-sex organ cancers

Project Purpose(s)

Disease Focused Research (cancer)
Population Health

Scientific Approaches

Frequencies and means, and appropriate t- and chi-squared tests; regression analyses (unadjusted and adjusted)

Anticipated Findings

Differences in self-reported outcomes listed above. We hope to better understand the experiences of discrimination of LGBTQIA+ patients with cancer in order to inform future interventions and drive hypotheses for future work.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Rachel Vogel - Mid-career Tenured Researcher, University of Minnesota
Patricia Jewett - Project Personnel, University of Minnesota
Allison Dona - Graduate Trainee, University of Minnesota

Cancer and healthcare discrimination

How does healthcare discrimination and social discrimination vary by cancer type, demographics, and other variables? How does this impact participant outcomes? Understanding who and in what context people who have been diagnosed with cancer experience healthcare discrimination is important to…

Scientific Questions Being Studied

How does healthcare discrimination and social discrimination vary by cancer type, demographics, and other variables? How does this impact participant outcomes? Understanding who and in what context people who have been diagnosed with cancer experience healthcare discrimination is important to improving equity in cancer care.
We will be looking at this both broadly by organ system as well as within organ systems including gynecologic malignancies

Project Purpose(s)

Disease Focused Research (cancer)
Population Health
Social / Behavioral

Scientific Approaches

Main predictor / exposure: cancer type, demographics, other variables
Main outcomes: self-reported survey. Including but not limited to QOL measures, healthcare and social discrimination, social support, and mortality.
Analysis:
- Describe outcome scores (means/frequencies; t-tests and chi-squared tests, non parametrics as appropriate)
- adjusted linear and logistic regression

Anticipated Findings

We hypothesize that individuals with certain cancers and marginalized backgrounds will experience more healthcare discriminations and worse outcomes. Potentially, some variation in QOL outcomes is explained by demographic characteristics, hence the adjusted analysis accounting for demographic and clinical factors.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Rachel Vogel - Mid-career Tenured Researcher, University of Minnesota
Patricia Jewett - Project Personnel, University of Minnesota
Allison Dona - Graduate Trainee, University of Minnesota

bids-2023-replicate

research question: association between psoriasis and asthma, and psoriasis and allergic rhinitis (AR) reason for exploring the data: previously paper indicate there are association between psoriasis and asthma, and psoriasis and allergic rhinitis. We would like to look further into…

Scientific Questions Being Studied

research question: association between psoriasis and asthma, and psoriasis and allergic rhinitis (AR)
reason for exploring the data: previously paper indicate there are association between psoriasis and asthma, and psoriasis and allergic rhinitis. We would like to look further into the dataset to validate the result. We wish to get a result that psoriasis is positively associated with asthma and AR
trying to replicate the paper:
https://link.springer.com/article/10.1007/s00403-023-02539-z

Joel, M.Z., Fan, R., Damsky, W. et al. Psoriasis associated with asthma and allergic rhinitis: a US-based cross-sectional study using the All of US Research Program. Arch Dermatol Res 315, 1823–1826 (2023). https://doi.org/10.1007/s00403-023-02539-z

Project Purpose(s)

Population Health

Scientific Approaches

trying to replicate the paper:
https://link.springer.com/article/10.1007/s00403-023-02539-z

Joel, M.Z., Fan, R., Damsky, W. et al. Psoriasis associated with asthma and allergic rhinitis: a US-based cross-sectional study using the All of US Research Program. Arch Dermatol Res 315, 1823–1826 (2023). https://doi.org/10.1007/s00403-023-02539-z

Anticipated Findings

trying to replicate the paper:
https://link.springer.com/article/10.1007/s00403-023-02539-z

Joel, M.Z., Fan, R., Damsky, W. et al. Psoriasis associated with asthma and allergic rhinitis: a US-based cross-sectional study using the All of US Research Program. Arch Dermatol Res 315, 1823–1826 (2023). https://doi.org/10.1007/s00403-023-02539-z

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

xueting wang - Graduate Trainee, Yale University

Medications Safety and Machine learning to Predict MACE and UDM

The study aims to investigate the safety and effectiveness of anti-diabetes medications in the United States. Due to the ever-changing trend in the management of diabetes, the contemporary safety and effectiveness of different approaches to diabetes become big concern. The…

Scientific Questions Being Studied

The study aims to investigate the safety and effectiveness of anti-diabetes medications in the United States. Due to the ever-changing trend in the management of diabetes, the contemporary safety and effectiveness of different approaches to diabetes become big concern. The approval of different classes of medications including Glucagon-like peptide-1 (GLP-1) agonists, peptidase inhibitors (DPP4 inhibitors), and sodium-glucose cotransporter inhibitors (SGLT2i) pose uncertainty in outcomes and safety of these medications. This study will explore the glycemic control, cardiovascular and renal outcomes of these medications, and their adverse health outcomes relative to the main stay therapy such as metformin and insulin. The main safety concerns include diabetic ketoacidosis, infections, cancer, and metabolic abnormalities. The study will also use predictive tools such as machine learning algorithms to predict outcomes in diabetes patients such as Uncontrolled Diabetes.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

To determine the safety and effectiveness, a cohort of patients who are taking anti-diabetes medications will be selected. The patients will be followed starting from the date of medication initiation till the current time. The incidence of different adverse effects and the status of patients will be recorded along with the types of medications. The specific events include glucose level, cardiovascular events, mortality, adverse renal outcomes, and adverse drug reactions such as diabetes ketoacidosis, infections, and cancer. Comparison will be made between groups who are taking different medications to determine the relative safety and effectiveness of the medications. We will implement different statistical and computational algorithms to determine the safety and effectiveness of anti-diabetes medications. particularly, the level of glycemic control will be predicted using machine learning approach.

Anticipated Findings

The assessment of the safety and effectiveness of anti-diabetes medications would help to select the appropriate therapy for diabetes patients. In addition, it will help to maximize the quality of life of patients and decrease the cost of treatment. The adverse effects of the medications would be early detected and prevented. Further to this, the macro-and microvascular complications of diabetes would be reduced. The early prediction of glycemic level will also help to prevent complications and to improve quality of life of diabetes patients.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Tadesse Abegaz - Graduate Trainee, Florida A&M University

WGS_TLtyping_Optimatization

The main objective is to measure telomere length through whole genome sequence data utilizing CRAM files. Additionally, the goal is to confirm the obtained telomere length by conducting GWAS and PheWAS analyses to determine if they can replicate previous findings…

Scientific Questions Being Studied

The main objective is to measure telomere length through whole genome sequence data utilizing CRAM files. Additionally, the goal is to confirm the obtained telomere length by conducting GWAS and PheWAS analyses to determine if they can replicate previous findings from GWAS and PheWAS studies on telomere length.

Project Purpose(s)

Methods Development
Ancestry

Scientific Approaches

The project aims to measure telomere length using whole genome sequencing data. Several Python and Java packages are available for this purpose, which utilize whole genome sequencing data. We will utilize these packages along with the All of Us cram files to estimate telomere length.

Anticipated Findings

The expected findings from this study include:
1. Telomere length measurement for each individual with available whole genome sequencing data.
2. Identification of known genetic variants associated with telomere length, as well as the possibility of discovering new variants.
3. Identification of reported and potentially new phenotypes associated with telomere length.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Patrick Allaire - Research Associate, Marshfield Clinic Research Institute
John Mayer - Project Personnel, Marshfield Clinic Research Institute
Rachel Gabor - Project Personnel, Marshfield Clinic Research Institute

WGS_TLtyping_paralelleWorkAround

The main objective is to measure telomere length through whole genome sequence data utilizing CRAM files. Additionally, the goal is to confirm the obtained telomere length by conducting GWAS and PheWAS analyses to determine if they can replicate previous findings…

Scientific Questions Being Studied

The main objective is to measure telomere length through whole genome sequence data utilizing CRAM files. Additionally, the goal is to confirm the obtained telomere length by conducting GWAS and PheWAS analyses to determine if they can replicate previous findings from GWAS and PheWAS studies on telomere length.

Project Purpose(s)

Methods Development
Ancestry

Scientific Approaches

The project aims to measure telomere length using whole genome sequencing data. Several Python and Java packages are available for this purpose, which utilize whole genome sequencing data. We will utilize these packages along with the All of Us cram files to estimate telomere length.

Anticipated Findings

The expected findings from this study include:
1. Telomere length measurement for each individual with available whole genome sequencing data.
2. Identification of known genetic variants associated with telomere length, as well as the possibility of discovering new variants.
3. Identification of reported and potentially new phenotypes associated with telomere length.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Patrick Allaire - Research Associate, Marshfield Clinic Research Institute

Collaborators:

John Mayer - Project Personnel, Marshfield Clinic Research Institute
Rachel Gabor - Project Personnel, Marshfield Clinic Research Institute

Diabetes in Middle Class American

There are 37.3 million people living with diabetes in the United States. The prevalence of diabetes among non-Hispanic Blacks is 11.7% compared to 7.5% among non-Hispanic Whites. Non-Hispanic Blacks are 60 percent more likely to be diagnosed with diabetes and…

Scientific Questions Being Studied

There are 37.3 million people living with diabetes in the United States. The prevalence of diabetes among non-Hispanic Blacks is 11.7% compared to 7.5% among non-Hispanic Whites. Non-Hispanic Blacks are 60 percent more likely to be diagnosed with diabetes and twice as likely as non-Hispanic Whites to die from diabetes. While socioeconomic status does play a role in the prevalence of diabetes, it's important to note that the relationship between income and diabetes prevalence is complex and influenced by many factors. Research has shown that diabetes prevalence is generally higher among individuals with lower income. However, little is known about the impact and prevalence of type 2 diabetes among middle-class non- Hispanic Blacks.

Project Purpose(s)

Disease Focused Research (Type 2 Diabetes)

Scientific Approaches

We plan to create two cohorts of subjects (non- Hispanic Blacks and other [white, Hispanic, etc.) representing middle class based on income, education, employment, insurance, and zip code.
We will use descriptive statistics (to describe the sample), as well as t-test (for comparisons between the two groups) and linear regression to assess the impact of covariates on glycemic outcomes (A1c & blood glucose).

Anticipated Findings

We hypothesize that there will be a difference between the prevalence of type 2 diabetes among middle-class, non- Hispanic Blacks compared to non- Hispanic Whites. We further hypothesize that there will be a difference in glycemic outcomes between the 2 groups.

These findings will contribute to the paucity of evidence regarding impact and prevalence of type 2 diabetes among middle class, non-Hispanic Blacks. The findings will also provide data to support the development of diabetes related interventions focus on this population.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Veronica Brady - Early Career Tenure-track Researcher, University of Texas Health Science Center, Houston

Collaborators:

Vivian Crowder - Graduate Trainee, University of Texas Health Science Center, Houston
Samuel Akyirem - Graduate Trainee, Yale University

bids-2023-analysis

research question: association between psoriasis and asthma, and psoriasis and allergic rhinitis (AR) reason for exploring the data: previously paper indicate there are association between psoriasis and asthma, and psoriasis and allergic rhinitis. We would like to look further into…

Scientific Questions Being Studied

research question: association between psoriasis and asthma, and psoriasis and allergic rhinitis (AR)
reason for exploring the data: previously paper indicate there are association between psoriasis and asthma, and psoriasis and allergic rhinitis. We would like to look further into the dataset to validate the result. We wish to get a result that psoriasis is positively associated with asthma and AR

Project Purpose(s)

Disease Focused Research (autoimmune diseases)
Population Health

Scientific Approaches

We will use univariate analysis and multivariate analysis to perform our research. We will use R, python, SQL for our research.

Anticipated Findings

We expect to get a result that psoriasis is positively associated with asthma and AR. By finding that, we can know more about the mechanism of immune diseases.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

xueting wang - Graduate Trainee, Yale University

Cystic Fibrosis

How does Fcy-R2A affect the prevalence of cystic fibrosis in young caucasian individuals in the United States?

Scientific Questions Being Studied

How does Fcy-R2A affect the prevalence of cystic fibrosis in young caucasian individuals in the United States?

Project Purpose(s)

Educational

Scientific Approaches

I will use the All of Us database to collect information on how Fcy-R2A affects the prevalence of cystic fibrosis.
I will collect statistics and research studies to help develop a hypothesis as to why Fcy-R2A affects auto immune diseases.

Anticipated Findings

My anticipated finding is that Fcy-R2A has a direct impact on the prevalence of cystic fibrosis in young caucasian individuals in the United States. This will be because of polymorphism in the Fcy receptor.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Sydney Sottek - Undergraduate Student, Arizona State University

Collaborators:

Anesa Ali - Undergraduate Student, Arizona State University

Cystic Fibrosis

How does fcygamma effect the prevalence of cystic fibrosis? How does fcygamma effect the prevalence of cystic fibrosis of young caucasian individuals in the United States?

Scientific Questions Being Studied

How does fcygamma effect the prevalence of cystic fibrosis?
How does fcygamma effect the prevalence of cystic fibrosis of young caucasian individuals in the United States?

Project Purpose(s)

Educational

Scientific Approaches

Scientific approaches being planned to use would mainly consists of the All Of Us research database. The research methods would consists of targeting how cystic fibrosis effects everyone and its prevalence in the caucasian population. The research methods would also target how the fcygamma receptor effects young caucasian individuals.

Anticipated Findings

Anticipated finding would revolve around the main group being effected by cystic fibrosis.
The findings would contribute to the body of scientific knowledge in the field by making others cognizant of the main effected group and what approaches need to be taken to prevent further complications and appearances of cystic fibrosis.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Anesa Ali - Undergraduate Student, Arizona State University

Demo workspace height example

N/A this is a demonstration workspace for education on how to use the AoU workspace This will demonstrate how to select a cohort of adult participants, search for height measurements and sex at birth data, plot a histogram of this…

Scientific Questions Being Studied

N/A this is a demonstration workspace for education on how to use the AoU workspace
This will demonstrate how to select a cohort of adult participants, search for height measurements and sex at birth data, plot a histogram of this data and perform a students t-test comparison of heights between males and females.
The intent is to create a "hello world" very simple example use of the AoU researchers workbench for beginners who have never used the workbench before, and have them perform their first analysis as rapidly as possible.

Project Purpose(s)

Educational

Scientific Approaches

N/A this is a demonstration workspace for education on how to use the AoU workspace
This will demonstrate how to select a cohort of adult participants, search for height measurements and sex at birth data, plot a histogram of this data and perform a students t-test comparison of heights between males and females.
The intent is to create a "hello world" very simple example use of the AoU researchers workbench for beginners who have never used the workbench before, and have them perform their first analysis as rapidly as possible.

Anticipated Findings

N/A this is a demonstration workspace for education on how to use the AoU workspace
This will demonstrate how to select a cohort of adult participants, search for height measurements and sex at birth data, plot a histogram of this data and perform a students t-test comparison of heights between males and females.
The intent is to create a "hello world" very simple example use of the AoU researchers workbench for beginners who have never used the workbench before, and have them perform their first analysis as rapidly as possible.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Stephen Richards - Other, Baylor College of Medicine

Collaborators:

Zachary Veitch - Project Personnel, Asian Health Coalition
Tiffany Reid - Project Personnel, W. Montague Cobb/NMA Health Institute
Riqiea Kitchens - Early Career Tenure-track Researcher, University of Texas Medical Branch (UTMB) at Galveston
Julie Coleman - Project Personnel, Baylor College of Medicine
Jinyoung Byun - Other, Baylor College of Medicine
Chitra Nayak - Mid-career Tenured Researcher, Tuskegee University
T.M. Ayodele Adesanya - Research Fellow, Ohio State University

Detecting Ovarian Cancer Early-Fall 2023

Can ovarian cancer be detected before diagnosis by machine learning? The 5-year survival of patients with epithelial ovarian cancer (EOC) is less than 40%. This is mainly due to patients having advanced disease (stages 3 or 4) by the time…

Scientific Questions Being Studied

Can ovarian cancer be detected before diagnosis by machine learning? The 5-year survival of patients with epithelial ovarian cancer (EOC) is less than 40%. This is mainly due to patients having advanced disease (stages 3 or 4) by the time it is diagnosed. However, 5-year survival of patients with stage 1 EOC is about 95%, with many even being cured. If EOC could be reliably detected earlier, then many more women could be cured than in current practice. Based on the lack of early detection, one might expect that women with early stage EOC don't show symptoms, but that is not true. It's just that symptoms tend to be weaker or nonspecific. Combined with the systemic barriers women face in obtaining an equal standard of care as men, these symptoms are often overlooked, dismissed, or misattributed. Machine learning is good at picking up on weak or diffuse signals in data. We believe it can produce a model that will detect EOC earlier than current practice, while it is still treatable.

Project Purpose(s)

Disease Focused Research (ovarian cancer)
Educational

Scientific Approaches

We plan to conduct a matched case-control study and analyze it with machine learning. The cases will be patients with ovarian cancer, and the controls will be similar patients (matched on demographics or propensity scores) without ovarian cancer. We will build binary classification models to distinguish between cases and controls, and then inspect their features to see what features are predictive of ovarian cancer. The features for a given patient will be all conditions, drugs, lab tests, procedures, vitals, and demographics before the time of cancer diagnosis. (Controls will have a matching censor date.) We will use classifiers from Scikit-Learn.

Anticipated Findings

We expect to validate the findings that ovarian cancer can be detected at an early stage by considering a collection of signs and symptoms (https://doi.org/10.1097/AOG.0000000000004642). The main products of the research will be models (binary classifiers) of ovarian cancer and a publication describing them, but we will consider our best-performing models for incorporation into a clinical decision support system, and may distill a model into a rule (or decision tree) for assessing ovarian cancer risk that would be simple enough for physicians to apply by hand. If developing such a simple but accurate rule is possible, then it could be of considerable use to doctors in screening for ovarian cancer and suggesting early treatment.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Aurod Ounsinegad - Graduate Trainee, University of Wisconsin, Madison

Duplicate of Explore Hypertension Data

This is an educational workspace for a training section to teach All of Us community about how to build a All of Us dataset. This workspace will study education, state and income of a hypertension cohort.

Scientific Questions Being Studied

This is an educational workspace for a training section to teach All of Us community about how to build a All of Us dataset. This workspace will study education, state and income of a hypertension cohort.

Project Purpose(s)

Educational

Scientific Approaches

A cohort of individuals with hypertension will be first built. Concept sets include education, income, and geographic state will be selected. Then a dataset will be built based on these concept sets for this cohort.

Anticipated Findings

The dataset of hypertension individuals will be built. The distribution of educations, incomes and geographic states of the individuals in this cohort can be analyzed later.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Riqiea Kitchens - Early Career Tenure-track Researcher, University of Texas Medical Branch (UTMB) at Galveston

BP_Meds_Genetics

The primary focus of the research is drug/therapeutics development. The data will be used to understand treatment-gene interactions or treatment outcomes relevant to the therapeutic(s) of interest. What sex or race differences exist in the efficacy of various medications? Based…

Scientific Questions Being Studied

The primary focus of the research is drug/therapeutics development. The data will be used to understand treatment-gene interactions or treatment outcomes relevant to the therapeutic(s) of interest. What sex or race differences exist in the efficacy of various medications? Based on the reasoning that many medications are tested in primarily white males. While the NIH has updated clinical trial guidelines, mice research is also primarily conducted in males.

Project Purpose(s)

Disease Focused Research (hypertension)

Scientific Approaches

Lab values/various predictors before and after beginning of medication. Will start by using cholesterol medications such as statins, and comparing LDL levels before and after prescription of statins in males vs females and across races/ethnicities

Anticipated Findings

I expect to find medications to be more efficacious in white men on the whole. However, there may be individual medication differences.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Slavina Goleva - Research Fellow, National Human Genome Research Institute (NIH-NHGRI)

Collaborators:

Peter Sauer - Other, National Human Genome Research Institute (NIH-NHGRI)
Ariel Williams - Research Fellow, National Human Genome Research Institute (NIH-NHGRI)

Detecting Ovarian Cancer Early

Can ovarian cancer be detected before diagnosis by machine learning? The 5-year survival of patients with epithelial ovarian cancer (EOC) is less than 40%. This is mainly due to patients having advanced disease (stages 3 or 4) by the time…

Scientific Questions Being Studied

Can ovarian cancer be detected before diagnosis by machine learning? The 5-year survival of patients with epithelial ovarian cancer (EOC) is less than 40%. This is mainly due to patients having advanced disease (stages 3 or 4) by the time it is diagnosed. However, 5-year survival of patients with stage 1 EOC is about 95%, with many even being cured. If EOC could be reliably detected earlier, then many more women could be cured than in current practice. Based on the lack of early detection, one might expect that women with early stage EOC don't show symptoms, but that is not true. It's just that symptoms tend to be weaker or nonspecific. Combined with the systemic barriers women face in obtaining an equal standard of care as men, these symptoms are often overlooked, dismissed, or misattributed. Machine learning is good at picking up on weak or diffuse signals in data. We believe it can produce a model that will detect EOC earlier than current practice, while it is still treatable.

Project Purpose(s)

Disease Focused Research (ovarian cancer)

Scientific Approaches

We plan to conduct a matched case-control study and analyze it with machine learning. The cases will be patients with ovarian cancer, and the controls will be similar patients (matched on demographics or propensity scores) without ovarian cancer. We will build binary classification models to distinguish between cases and controls, and then inspect their features to see what features are predictive of ovarian cancer. The features for a given patient will be all conditions, drugs, lab tests, procedures, vitals, and demographics before the time of cancer diagnosis. (Controls will have a matching censor date.) We will use classifiers from Scikit-Learn.

Anticipated Findings

We expect to validate the findings that ovarian cancer can be detected at an early stage by considering a collection of signs and symptoms (https://doi.org/10.1097/AOG.0000000000004642). The main products of the research will be models (binary classifiers) of ovarian cancer and a publication describing them, but we will consider our best-performing models for incorporation into a clinical decision support system, and may distill a model into a rule (or decision tree) for assessing ovarian cancer risk that would be simple enough for physicians to apply by hand. If developing such a simple but accurate rule is possible, then it could be of considerable use to doctors in screening for ovarian cancer and suggesting early treatment.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Irene Ong - Early Career Tenure-track Researcher, University of Wisconsin, Madison
Aubrey Barnard - Research Fellow, University of Wisconsin, Madison

Collaborators:

Sierra Strutz - Graduate Trainee, University of Wisconsin, Madison
Aurod Ounsinegad - Graduate Trainee, University of Wisconsin, Madison

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