Research Projects Directory

Research Projects Directory

3,097 active projects

This information was updated 11/27/2022

The Research Projects Directory includes information about all projects that currently exist in the Researcher Workbench to help provide transparency about how the Workbench is being used. Each project specifies whether Registered Tier or Controlled Tier data are used.

Note: Researcher Workbench users provide information about their research projects independently. 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.

117 projects have 'diabetes' in the project title
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DUPLICATE:Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

  • Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Deep Metric Learning for Diabetes Subtyping

The International Diabetes Federation estimates that 10% of the world's population will have diabetes by 2035. Patients living with diabetes are at higher risk for many acute and chronic complications, which may lead to increased hospital or ED visits. Accurate…

Scientific Questions Being Studied

The International Diabetes Federation estimates that 10% of the world's population will have diabetes by 2035. Patients living with diabetes are at higher risk for many acute and chronic complications, which may lead to increased hospital or ED visits. Accurate subtyping of those with type 2 diabetes is crucial to understand what characteristics of patients lead to increased risk of adverse outcomes, and is key to more effective and targeted treatments of diabetes and its complications.

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus)
  • Methods Development

Scientific Approaches

We will make use of a machine learning method called Deep Metric Learning (DML). DML seeks to learn a representation of the patient's state by maximizing its similarity with other patients with the same label. DML has previously been shown to be effective in subtyping several diseases in the medical imaging domain. However, DML has not been widely used on Electronic Health Records (EHR) and genetic data. Here, we propose using DML to learn subtypes for type 2 diabetes using time-series data from the EHR, as well as survey and genetic data.

Anticipated Findings

We anticipate that the DML representations we learn will form natural clusters corresponding to patient subtypes. We anticipate that patients in particular subtypes will exhibit similarities based on their input features (i.e. demographics, labs, vitals, surveys, or genetics). Patients in different subtypes may also have different outcomes (i.e. # hospital visits, complications, mortality). We believe that characterizing such subtypes will be useful for clinicians to provide targeted treatments to different patients, which may improve health outcomes. Characterizing such subtypes may also be useful in more accurate diagnosis of diabetes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Qixuan Jin - Graduate Trainee, Massachusetts Institute of Technology
  • Haoran Zhang - Graduate Trainee, Massachusetts Institute of Technology

Disparities in Type 1 Diabetes Mellitus Technologies and Medical Management

Insulin pump systems offer improved glycemic control and reduced morbidity and mortality in Type 1 Diabetes Mellitus (T1DM). In recent years, insulin pumps have increasingly been paired with continuous glucose monitors (CGMs) to automate insulin delivery and reduce user input.…

Scientific Questions Being Studied

Insulin pump systems offer improved glycemic control and reduced morbidity and mortality in Type 1 Diabetes Mellitus (T1DM). In recent years, insulin pumps have increasingly been paired with continuous glucose monitors (CGMs) to automate insulin delivery and reduce user input. Significant barriers to diabetes technologies–including cost, access, and provider knowledge–limit uptake. Similar barriers also likely exist for the use of oral antihyperglycemic medications that require investigation of insulin resistance profiles in patients receiving care at specialized centers. The goal of this study is to identify socioeconomic disparities in T1DM management in a large national cohort. Specifically, we aim to (1) characterize insulin pump system use across race/ethnicity among patients in the United States, (2) characterize insulin pump complications among patients in the United States, and (3) characterize oral anti-hyperglycemic medicationn use in T1DM using the All of Us database.

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus)

Scientific Approaches

We will perform a retrospective review of the All of Us database of all patients with Type 1 Diabetes Mellitus and use multivariable analysis to identify factors associated with insulin pump use, insulin pump complications, and use of oral anti-hyperglycemic medications. Patients with a history of Type 2 Diabetes Mellitus will be excluded from this study.

Anticipated Findings

We expect to find that patients from historically disadvantaged populations will be less likely to receive insulin pump therapy in T1DM and less likely to receive oral hypoglycemics as part of their diabetes regimen.

Demographic Categories of Interest

  • Race / Ethnicity
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

  • Tyler Ryan - Graduate Trainee, University of Massachusetts Medical School
  • Ben Gerber - Late Career Tenured Researcher, University of Massachusetts Medical School

Risk of CKD in Diabetes

We plan to use the existing knowledge about genes involved in diabetes to create a risk model, using those genetic variants and clinical data for the development of chronic kidney disease and/or anemia in diabetic patients. In addition, we plan…

Scientific Questions Being Studied

We plan to use the existing knowledge about genes involved in diabetes to create a risk model, using those genetic variants and clinical data for the development of chronic kidney disease and/or anemia in diabetic patients. In addition, we plan to validate our findings regarding a clinical risk score for development of CKD on All of us data.

Project Purpose(s)

  • Disease Focused Research (Chronic kidney disease and anemia in diabetes)
  • Methods Development
  • Ancestry

Scientific Approaches

We are going to select a cohort of patients with diabetes. We are going to extract a list of relevant clinical and genomics information and do risk modeling using methods like regression, decision trees, and deep learning. We are going to use python for analysis and also use commonplace machine leaning libraries including sci-kit learn.

Anticipated Findings

We anticipate our risk score to help clinicians stratify their patients based on the likelihood of developing CKD or anemia. In addition, these findings can help selection of more suited cohorts for clinical trials about CDK/anemia in diabetic patients.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Trust Odia - Research Fellow, Institute for Systems Biology
  • Sevda Molani - Research Fellow, Institute for Systems Biology
  • Alexandra Ralevski - Research Fellow, Institute for Systems Biology

Reproducing the results of a UK Biobank study on Type 2 Diabetes

The study I am trying to replicate attempted to determine whether genetic ancestry and socioeconomic deprivation (SED) interact to affect one's risk of developing type 2 diabetes and whether this interaction varies depending on genetic ancestry type. Such insight may…

Scientific Questions Being Studied

The study I am trying to replicate attempted to determine whether genetic ancestry and socioeconomic deprivation (SED) interact to affect one's risk of developing type 2 diabetes and whether this interaction varies depending on genetic ancestry type. Such insight may help inform future health interventions.

Project Purpose(s)

  • Other Purpose (I am a research trainee trying to familiarize myself with both the All of Us workspace and working with All of Us data. To accomplish this, I will be attempting to replicate the results of a study conducted by my lab, which was published as an article titled "Socioeconomic deprivation and genetic ancestry interact to modify type 2 diabetes ethnic disparities in the United Kingdom". )

Scientific Approaches

Case / control cohorts will be made with individuals with type 2 diabetes and those without the disease, respectively. Analyses with be done in Python and R.

Anticipated Findings

The results are expected to those of the study I am attempting to replicate - socioeconomic deprivation is expected to increase the likelihood of developing type 2 diabetes among all demographic groups, but this effect is expected to be especially pronounced in individuals of African and Asian ancestry.

Demographic Categories of Interest

  • Race / Ethnicity
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

  • Vincent Lam - Research Fellow, National Institutes of Health (NIH)

Collaborators:

  • Shivam Sharma - Graduate Trainee, Georgia Institute of Technology
  • Robin Kee - Graduate Trainee, National Institutes of Health (NIH)
  • Leonardo Marino-Ramirez - Senior Researcher, National Institutes of Health (NIH)

Race differences in pathology of diabetes

To compare pattern of insulin reistance ateration in development and progression to diabetes, between White and Black race.

Scientific Questions Being Studied

To compare pattern of insulin reistance ateration in development and progression to diabetes, between White and Black race.

Project Purpose(s)

  • Disease Focused Research (Type 2 diabetes mellitus)

Scientific Approaches

The dataset we would like to utilize would be participants’ provided information (PPI), electronic health records (EHR) and physical measurements, and surveys which provide information about the participant’s overall health status, lifestyle, medication history, serum biochemicals and demographic characteristics.

Anticipated Findings

We anticipate that there are different patterns in terms of insulin resistance alteration for White and Balck race. The result could identify possible race-specific screening, diagnosis, prevention, and treatment strategies.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Registered Tier

Research Team

Owner:

Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

  • Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Taotao Tan - Other, Baylor College of Medicine
  • Elizabeth Atkinson - Early Career Tenure-track Researcher, Baylor College of Medicine
  • Xyanthine Parillon - Other, Baylor College of Medicine
  • Jun Wang - Other, Baylor College of Medicine
  • Victoria Mgbemena - Early Career Tenure-track Researcher, Baylor College of Medicine
  • Varuna Chander - Graduate Trainee, Baylor College of Medicine
  • Jordan Booker - Early Career Tenure-track Researcher, Baylor College of Medicine
  • Titilayo Olubajo - Research Fellow, Baylor College of Medicine
  • Erik Stricker - Graduate Trainee, Baylor College of Medicine
  • Sangeeta Tiwari - Early Career Tenure-track Researcher, University of Texas at El Paso
  • Shamika Ketkar - Other, Baylor College of Medicine
  • Sabur Badmos - Research Fellow, University of Texas at El Paso
  • TAGARI SAMANTA - Research Fellow, Baylor College of Medicine
  • Robert Petrovic - Graduate Trainee, Baylor College of Medicine
  • Renita Horton - Early Career Tenure-track Researcher, University of Houston
  • Zaida Ramirez-Ortiz - Early Career Tenure-track Researcher, University of Massachusetts Medical School
  • Pamela Luna - Other, Baylor College of Medicine
  • Nirav Shah - Graduate Trainee, Baylor College of Medicine
  • Nilsson Holguin - Early Career Tenure-track Researcher, Icahn School of Medicine at Mount Sinai
  • Nyasha Chambwe - Early Career Tenure-track Researcher, Feinstein Institute for Medical Research
  • Chang In Moon - Graduate Trainee, Baylor College of Medicine
  • Leslie Johnson - Early Career Tenure-track Researcher, Emory University
  • Lesley Chapman Hannah - Research Fellow, National Cancer Institute (NIH - NCI)
  • Luisa Cervantes-Barragan - Early Career Tenure-track Researcher, Emory University
  • Lalita Wadhwa - Other, Baylor College of Medicine
  • Kim Worley - Other, Baylor College of Medicine
  • Kevin Wilhelm - Graduate Trainee, Baylor College of Medicine
  • Kimiko Krieger - Research Fellow, Baylor College of Medicine
  • Panagiotis Katsonis - Other, Baylor College of Medicine
  • Jose Nolazco - Research Fellow, Baylor College of Medicine
  • Jinyoung Byun - Other, Baylor College of Medicine
  • Joyonna Gamble-George - Research Fellow, New York University
  • Jasmine Baker - Research Fellow, Baylor College of Medicine
  • Janitza Montalvo-Ortiz - Early Career Tenure-track Researcher, Yale University
  • Heather Danhof - Other, Baylor College of Medicine
  • Paola Giusti-Rodriguez - Other, University of Florida
  • Gary Huang - Graduate Trainee, Baylor College of Medicine
  • Stephen Richards - Other, Baylor College of Medicine
  • Fei Yue - Research Fellow, Baylor College of Medicine
  • Fatemeh Choupani - Research Fellow, University of Washington
  • Erick Olivares Bravo - Research Fellow, University of Texas, San Antonio
  • Emily Jackson-Osagie - Early Career Tenure-track Researcher, Southern University and A&M College
  • Elisa Marroquin - Early Career Tenure-track Researcher, Texas Christian University
  • Deyana Lewis - Research Fellow, National Institutes of Health (NIH)
  • Dawanna White - Early Career Tenure-track Researcher, Hampton University
  • Cathy Samayoa - Research Fellow, University of California, San Francisco
  • Charcacia Sanders - Other, Baylor College of Medicine
  • Catherine Ann Gavile - Research Fellow, University of Utah
  • Carlos Eduardo Guerra Amorim - Early Career Tenure-track Researcher, California State University, Northridge
  • Yuan Yao - Project Personnel, Baylor College of Medicine
  • Andrea Wilderman - Research Fellow, Baylor College of Medicine
  • Amy Adams - Other, University of South Alabama
  • Adriana Visbal - Early Career Tenure-track Researcher, Baylor College of Medicine

Educational run of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

  • Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Jun Wang - Other, Baylor College of Medicine

Basis of Diabetes and Diabetes-related Complications

The primary goal of the proposed analyses is to understand the genetic basis of diabetes and diabetes-related complications, including kidney disease, as well as to understand the genetic factors that contribute to the increased risk of these diseases among under-represented…

Scientific Questions Being Studied

The primary goal of the proposed analyses is to understand the genetic basis of diabetes and diabetes-related complications, including kidney disease, as well as to understand the genetic factors that contribute to the increased risk of these diseases among under-represented populations.

Project Purpose(s)

  • Disease Focused Research (Diabetes and diabetic complications)

Scientific Approaches

We plan to leverage the entire All of Us cohort to better understand the genetic basis of diabetes and diabetic complications, including kidney disease. We will use genetic data in All of Us to identify associations with diabetes and diabetic complications as well as to validate candidate variants and/or genes that emerge from our own studies being conducted in cohorts developed at the University of Utah.

Anticipated Findings

We anticipate that the findings of our study will aid in understanding the genetic basis of diabetes and diabetic complications and identify genetic factors that contribute to the increased risk of these diseases among under-represented populations. We anticipate that these findings will identify new genes and/or pathways that contribute to diabetes and its complications that may lead to improved diagnostics, surveillance, and treatment of these diseases.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Devorah Stucki - Project Personnel, University of Utah

Safety and Effectiveness of Anti-diabetes Medications

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 concerns. 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 concerns. 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.

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.

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.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

Diabetes

People from different group suffered diabetes disproportionately, what is the key risk factors associated with diabetes?

Scientific Questions Being Studied

People from different group suffered diabetes disproportionately, what is the key risk factors associated with diabetes?

Project Purpose(s)

  • Disease Focused Research (Diabetes)
  • Educational

Scientific Approaches

We will focus on patients who were 18 years old or older, collect risk factors from EHR and survey, build statistical models and identify risk factors.

Anticipated Findings

We expect to see the social determinant health factors as well as clinical characteristics that had strong association with diabetes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Aize Cao - Early Career Tenure-track Researcher, Meharry Medical College

Associations of Cognitive Dysfunction with Diabetes

Cognitive dysfunction can increase oxidative stress and mitochondrial dysfunction which are linked to impaired IGF/insulin signaling leading to hyperglycemia. Within this study, we aim to assess risk factors, such as cognitive dysfunction, that lead to diabetes to better assess and…

Scientific Questions Being Studied

Cognitive dysfunction can increase oxidative stress and mitochondrial dysfunction which are linked to impaired IGF/insulin signaling leading to hyperglycemia. Within this study, we aim to assess risk factors, such as cognitive dysfunction, that lead to diabetes to better assess and manage the risk in the diabetes population. To this end, the objective of this study is to determine the associations of cognitive dysfunction with increased risk of diabetes.

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus, alzheimer's disease)

Scientific Approaches

Logistic regression models will be used to select predictors of diabetic diagnosis from potential candidate variables, including demographics, general health, behavior and lifestyle, disease history, lab and prescription medications. First, univariable association of all the potential predictors will be evaluated (i.e., entered into the survey logistic regression models one at a time), and those with statistically significant association will then be entered together into a multivariable regression model. The final multivariable model will retain all variables that remain statistically significant. Statistical significance will be assessed by two-sided P values of< 0.05, with no adjustments for multiple testing. Odds ratio (OR) and 95 % confidence intervals (CI) will be estimated and presented for predictors selected in the final models.

Anticipated Findings

We anticipate a correlation between presence of cognitive dysfunction and increase risk of diabetes wherein this will allow for preventative screening and patient care.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Joan LLabre - Graduate Trainee, Rensselaer Polytechnic Institute

Type 2 Diabetes & Platelet Count

The specific scientific research question that I intend to study is whether or not there is a correlation between type 2 diabetes mellitus and the variation in individuals' platelet counts. This question is important because it can potentially help to…

Scientific Questions Being Studied

The specific scientific research question that I intend to study is whether or not there is a correlation between type 2 diabetes mellitus and the variation in individuals' platelet counts. This question is important because it can potentially help to develop a diagnostic tool that is derived from complete blood counts, which provides platelet counts.

Project Purpose(s)

  • Disease Focused Research (Type II diabetes)
  • Educational

Scientific Approaches

I plan to create a dataset containing information about the platelet counts within individuals who either have type 2 diabetes mellitus or do not. I plan to use R as a statistical package to analyze the data.

Anticipated Findings

My anticipated findings are that those with type 2 diabetes mellitus will be more likely to have lower platelet levels than those without type 2 diabetes mellitus. My findings would help to contribute to this body of scientific knowledge in the field by validating this in the All of Us database, which may have been validated in other databases, prior.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Stevan Lazic - Undergraduate Student, University of Wisconsin, La Crosse

CAC - Type 2 Diabetes GWAS Demo

This notebook will be used to practice building cohort using big query and then run a GWAS analysis with Hail. The case study will focus on type 2 diabetes. The main purpose is learning the mentioned methods.

Scientific Questions Being Studied

This notebook will be used to practice building cohort using big query and then run a GWAS analysis with Hail. The case study will focus on type 2 diabetes. The main purpose is learning the mentioned methods.

Project Purpose(s)

  • Educational

Scientific Approaches

Data will be selected using big query following Type 2 Diabetes algorithm on PheKB. Selected data will then be used for GWAS analysis with Hail on Google Cloud.

Anticipated Findings

Anticipated findings would be showing relevant SNPs associated with type 2 diabetes as the results of GWAS analysis. This case study is only to learn using big query and Hail.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Tam Tran - Other, National Institutes of Health (NIH)

Diabetes physiological

At this stage I am currently exploring the data to formalize research questions comparing: Hispanics and non-Hispanic Blacks with diabetes who are 45 years and older; and English- and Spanish-dominant Hispanics Whites, Hispanic Blacks and Hispanic multiracial groups with diabetes.…

Scientific Questions Being Studied

At this stage I am currently exploring the data to formalize research questions comparing: Hispanics and non-Hispanic Blacks with diabetes who are 45 years and older; and English- and Spanish-dominant Hispanics Whites, Hispanic Blacks and Hispanic multiracial groups with diabetes. Because Hispanics and non-Hispanic Blacks have a high rate of diabetes compared to Whites it is important to examine changes, shifts and trends of diabetes and diabetes-related conditions among different ethnic groups.

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus)
  • Population Health
  • Social / Behavioral
  • Control Set
  • Ancestry

Scientific Approaches

Comparison of physiological variables (e.g., A1c level, blood pressure ranges, cholesterol and triglyceride levels, body mass index); and examine genetic differences among Hispanics and non-Hispanic Blacks.

Anticipated Findings

For certain physiological variables Hispanics will have higher rates than non-Hispanic Blacks and for other variables Hispanics will have lower rates compared to non-Hispanic Blacks with diabetes; Spanish-dominant Hispanics with diabetes will have overall higher rates of the physiological variables compared to English-dominant adults; and when comparing English- and Spanish-dominant Hispanic Whites, Hispanic Blacks and Hispanic multiracial groups with diabetes, Spanish-dominant Hispanic Blacks and multiracial groups will have higher rates than Hispanic Whites.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Geography
  • Access to Care
  • Education Level
  • Income Level
  • Others

Data Set Used

Registered Tier

Research Team

Owner:

  • Judith Aponte - Mid-career Tenured Researcher, City University of New York (CUNY)

Decision Tree Classifier with COVID-19 and Diabetes Mellitus Type-2

Hypothesis: COVID-19 worsens Diabetes Mellitus Type-2. The reasons to explore participant data is to determine if their is any correlation between COVID-19 and worsening of Diabetes Mellitus Type-2.

Scientific Questions Being Studied

Hypothesis: COVID-19 worsens Diabetes Mellitus Type-2.
The reasons to explore participant data is to determine if their is any correlation between COVID-19 and worsening of Diabetes Mellitus Type-2.

Project Purpose(s)

  • Disease Focused Research (Correlation between COVID-19 and worsening of Diabetes Mellitus Type-2.)
  • Population Health

Scientific Approaches

Datasets:
- All of Us data: disease conditions (condition labels and condition IDs).

Methods:
- Train and test a decision tree classifier model with condition IDs.
- Create a mapping between All of Us OMOP condition IDs to the condition labels.
- Export the decision tree classifier as JavaScript Object Notation (JSON).
- Map the OMOP condition IDs to the condition labels for human readability of the decision tree classifier model.
- Visualize the decision tree classifier model with D3.js.

Software:
- Programming languages: Python3, JavaScript, CSS3, HTML

Anticipated Findings

Anticipated findings:
- That there is correlation between COVID-19 and a worsening of Diabetes Mellitus Type-2.

Demographic Categories of Interest

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

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • Carina Grunberg - Project Personnel, AI LA Community, Inc

Diabetes_retinopathy

How do EHR-based cohorts selected for DR in scientific studies vary demographically and clinically?

Scientific Questions Being Studied

How do EHR-based cohorts selected for DR in scientific studies vary demographically and clinically?

Project Purpose(s)

  • Disease Focused Research (diabetic retinopathy)

Scientific Approaches

We plan to use data from the All of Us study for patients identified with diabetic retinopathy based on different criteria from previously published studies.

Anticipated Findings

We anticipate that different studies will have selected varying EHR-based criteria for DR that result in different populations studied.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Jimmy Chen - Research Fellow, University of California, San Diego

Type 2 Diabetes: PRS and Family History

We intend to investigate the predictive power of polygenic risk (PRS) for type 2 diabetes and the additive power of family history of type 2 diabetes. We also intend to determine the differences in predictive power of PRS and family…

Scientific Questions Being Studied

We intend to investigate the predictive power of polygenic risk (PRS) for type 2 diabetes and the additive power of family history of type 2 diabetes. We also intend to determine the differences in predictive power of PRS and family history among different ancestry groups.

Project Purpose(s)

  • Disease Focused Research (Type 2 Diabetes)
  • Population Health
  • Ancestry

Scientific Approaches

We intend to use the genetic data and family history data in order to address this question.

Anticipated Findings

The anticipated findings are that family history will add more predictive power in non-European populations due to PRS tending to perform worse in non-European populations.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

Diabetes GWAS on African American

What are the variants associated with type 2 diabetes and with glocose and hba1c levels within the african american cohort and european cohorrts? What are the genes involved? How does it differ from similar GWAS results based on other populations?…

Scientific Questions Being Studied

What are the variants associated with type 2 diabetes and with glocose and hba1c levels within the african american cohort and european cohorrts?
What are the genes involved?
How does it differ from similar GWAS results based on other populations?
How does it compare to previous studies on the same population?

Project Purpose(s)

  • Disease Focused Research (Diabetes)
  • Methods Development
  • Ancestry

Scientific Approaches

Using the WGS data and the conditions and demographics data, we will look at individuals with all individuals, african-american or european heritage (self declared) and create a phenotype as follows:

if the individual has a condition with "Type 2 diabetes" and doesn't have a condition with "type 1 diabetes" ==> CASE
if the individual has "diabetes" but wasn't considered a CASE ==> DROP
the remaining individials ==> CONTROL

For genotypes we will examine all the (biallelic) variants that have a high enough allele frequency, and have already been through the AoU sample and variant QC steps, and run a GWAS comparing the association of each variants with the phenotype (CASE/CONTROL) using a linear model. additional covariates will be SEX, AGE, PCA loadings and possibly BMI. If we can correct for the recruitment center, we will.

For glocose and hba1c we will perform a continuous trait GWAS.

Anticipated Findings

Anticipated finding are:

a GWAS summary stats table,
a list of genes that are already known to be associate with type 2 diabetes,
a list of genes that now are newly significant for being associated with type 2 diabetes
a list of genes that might become significant for type 2 diabetes after meta analysis with other similar studies.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

Type 2 Diabetes Subtyping

The effects of different blood components on glucose levels in hyperglycemic patients. We want to create a predictive model which can estimate change in level of glucose by changing blood component concentration, such as RBC counts, electrolyte concentration, etc. ,…

Scientific Questions Being Studied

The effects of different blood components on glucose levels in hyperglycemic patients. We want to create a predictive model which can estimate change in level of glucose by changing blood component concentration, such as RBC counts, electrolyte concentration, etc. , in Type 2 diabetes patients.

Project Purpose(s)

  • Disease Focused Research (hyperglycemia)

Scientific Approaches

We will analyze the lab results of 150,000 plus individuals in the All Of Us Research workbench. We will try to find a pattern between glucose levels compared to blood components levels in normal and diabetic patients in Python and determine which blood component is associated with change in blood glucose levels. Once we determine these components, we want to create a predictive model which will determine which blood component can assist in blood glucose maintenance.

Anticipated Findings

The results of our study, once published, will assist doctors in making better decisions to regulate glucose levels with drugs, dietary, and lifestyle changes as well as if any medication changes levels of any blood components, our model can predict how it will affect glucose levels in the patients.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Tier 5 - Type 2 Diabetes Subtyping

The effects of different blood components on glucose levels in hyperglycemic patients. We want to create a predictive model which can estimate change in level of glucose by changing blood component concentration, such as RBC counts, electrolyte concentration, etc. ,…

Scientific Questions Being Studied

The effects of different blood components on glucose levels in hyperglycemic patients. We want to create a predictive model which can estimate change in level of glucose by changing blood component concentration, such as RBC counts, electrolyte concentration, etc. , in Type 2 diabetes patients.

Project Purpose(s)

  • Disease Focused Research (hyperglycemia)

Scientific Approaches

We will analyze the lab results of 150,000 plus individuals in the All Of Us Research workbench. We will try to find a pattern between glucose levels compared to blood components levels in normal and diabetic patients in Python and determine which blood component is associated with change in blood glucose levels. Once we determine these components, we want to create a predictive model which will determine which blood component can assist in blood glucose maintenance.

Anticipated Findings

The results of our study, once published, will assist doctors in making better decisions to regulate glucose levels with drugs, dietary, and lifestyle changes as well as if any medication changes levels of any blood components, our model can predict how it will affect glucose levels in the patients.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Type II Diabetes and Platelet Count

The specific scientific question I intend to study is whether or not there is a correlation between adverse health outcomes with an individual's platelet count and if so, what type of correlation is present.

Scientific Questions Being Studied

The specific scientific question I intend to study is whether or not there is a correlation between adverse health outcomes with an individual's platelet count and if so, what type of correlation is present.

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus)
  • Educational

Scientific Approaches

I plan to create a dataset containing information about the platelet counts within individuals who either have type 2 diabetes mellitus or do not. I plan to use R as a statistical package to analyze the data.

Anticipated Findings

My anticipated findings are that those with type 2 diabetes mellitus will be more likely to have lower platelet levels than those without type 2 diabetes mellitus. My finding would help to contribute to the body of scientific knowledge in the field by validating this in the All of Us database, which may have been validated in other databases, prior.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Stevan Lazic - Undergraduate Student, University of Wisconsin, La Crosse

Type 2 Diabetes GWAS Demo

This notebook will be used to practice building cohort using big query and then run a GWAS analysis with Hail. The case study will focus on type 2 diabetes. The main purpose is learning the mentioned methods.

Scientific Questions Being Studied

This notebook will be used to practice building cohort using big query and then run a GWAS analysis with Hail. The case study will focus on type 2 diabetes. The main purpose is learning the mentioned methods.

Project Purpose(s)

  • Educational

Scientific Approaches

Data will be selected using big query following Type 2 Diabetes algorithm on PheKB. Selected data will then be used for GWAS analysis with Hail on Google Cloud.

Anticipated Findings

Anticipated findings would be showing relevant SNPs associated with type 2 diabetes as the results of GWAS analysis. This case study is only to learn using big query and Hail.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Tam Tran - Other, National Institutes of Health (NIH)

Collaborators:

  • Tracey Ferrara - Project Personnel, National Institutes of Health (NIH)
  • Huan Mo - Research Fellow, National Institutes of Health (NIH)
  • Jian Dai - Project Personnel, National Institutes of Health (NIH)
  • Slavina Goleva - Research Fellow, National Institutes of Health (NIH)
  • David Schlueter - Research Fellow, National Institutes of Health (NIH)
  • Chenjie Zeng - Research Fellow, National Institutes of Health (NIH)
  • Ariel Williams - Research Fellow, National Institutes of Health (NIH)
  • Anav Babbar - Other, National Institutes of Health (NIH)
  • Anas Awan - Project Personnel, National Institutes of Health (NIH)

Duplicate of Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

  • Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

COVID-19 and Diabetes - Controlled Tier

Hypothesis: People who have had COVID-19 are at greater risk for type 2 diabetes. We want to build an easy-to-use interactive dashboard for users to understand how the COVID-19 pandemic may have impacted their health with respect to diabetes. We…

Scientific Questions Being Studied

Hypothesis: People who have had COVID-19 are at greater risk for type 2 diabetes. We want to build an easy-to-use interactive dashboard for users to understand how the COVID-19 pandemic may have impacted their health with respect to diabetes. We aim to quantify to how the presence of COVID-19 (whether an individual personally experienced the illness or not) affected the presence of diabetes and diabetes related symptoms and co-morbidities.

Project Purpose(s)

  • Educational

Scientific Approaches

Exploratory data analysis followed by feature engineering, model development and testing, culminating in an interactive dashboard. Python will be used for all data analysis (especially the pandas and numpy libraries), machine learning models (scikitlearn), and the dashboard (matplotlib, seaborn, plotly, dash).

Anticipated Findings

Anticipated findings - a correlation between COVID-19 and diabetes with a possibility of linking causation in cases where COVID-19 test positivity preceded diabetes type 2 diagnosis or exacerbation of previously diagnosed diabetes. External research to posit explanations for this relationship.

Demographic Categories of Interest

  • Race / Ethnicity
  • Education Level

Data Set Used

Controlled Tier

Research Team

Owner:

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