Research Projects Directory

Research Projects Directory

10,053 active projects

This information was updated 3/29/2024

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.

323 projects have 'diabetes' in the project title
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=Type 2 Diabetes: PRS and Family History with version 7

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:

Collaborators:

  • Reagan Ballard - Undergraduate Student, University of North Carolina, Chapel Hill
  • Micah Hysong - Graduate Trainee, University of North Carolina, Chapel Hill

CaseType2 Phenotype - Type 2 Diabetes (v6)

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research, using the Controlled Tier Curated Data Repository (CDR).

Scientific Questions Being Studied

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research, using the Controlled Tier Curated Data Repository (CDR).

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus)
  • Educational
  • Methods Development
  • Other Purpose (This is an All of Us Phenotype Library Workspace created by the Researcher Workbench Support team. It is meant to demonstrate the implementation of key phenotype algorithms within the All of Us Research Program cohort, using the Controlled Tier Curated Data Repository (CDR).)

Scientific Approaches

Controlled-tier All of Us cohort data; Jupyter Notebooks, Cohort Builder, Concept Set Selector, Dataset Selector

Anticipated Findings

By reading and running the Notebooks in this Phenotype Library Workspace, researchers can implement the following phenotype algorithms: Jennifer Pacheco and Will Thompson. Northwestern University. Type 2 Diabetes Mellitus. PheKB; 2012 Available from: https://phekb.org/phenotype/18

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Razaq Durodoye - Graduate Trainee, Case Western Reserve University
  • Jacqueline Shaia - Graduate Trainee, Case Western Reserve University
  • Jacob Rich - Graduate Trainee, Case Western Reserve University
  • Christopher Maatouk - Graduate Trainee, Case Western Reserve University

Genetics of Diabetes and its related Metabolic Traits

Hundreds of genetic variants have been linked with diabetes and related metabolic conditions, including for obesity, type-two diabetes, HbA1c (glycated haemoglobin).. However, the biological pathways by which they act is not yet clear, nor have all causal genetic variants been…

Scientific Questions Being Studied

Hundreds of genetic variants have been linked with diabetes and related metabolic conditions, including for obesity, type-two diabetes, HbA1c (glycated haemoglobin).. However, the biological pathways by which they act is not yet clear, nor have all causal genetic variants been identified, especially those in the fewest individuals. We intend to run genome-wide association studies between these phenotypes (e.g. individuals who suffer from diabetes versus those who do not, or BMI as a continuous trait), and other diabetes-related phenotypes (HbA1c, glucose etc) against genetic variants from the Whole Genome Sequencing data. Our results will improve the field’s understanding of the biological pathways for these conditions. The All of Us data set is crucial for answering these questions, due to its diverse genetic ancestry. Whole genome sequencing contains nearly all genetic variants, down to the rarest (<1% of individuals), which aids our ability to identify causal genetic mutations.

Project Purpose(s)

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

Scientific Approaches

We will be performing genome-wide association studies, comparing the frequency of variants between individuals who do and do not suffer from diabetes and other complications, or looking to identify variants that are associated with increased or decreased BMI/HbA1c. We will use the software tool REGENIE to perform our analysis, which rigorously controls for known confounders. Our association analyses will be performed using the most recent release of whole-genome sequencing data. We will also perform downstream analyses, such as Fine Mapping, which inform us about which variants are and are not causal.

Anticipated Findings

We anticipate finding novel genetic factors associated with metabolic and diabetes-related diseases. Our results will be published in high-impact journals under an open-access agreement.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Harry Wright - Research Assistant, University of Exeter
  • Harry Green - Mid-career Tenured Researcher, University of Exeter

Insights into Anemia and Associated Factors in Type 2 Diabetes Patient Cohorts

The project aims to investigate and assess the prevalence of anemia and associated factors among female adults and elderly individuals diagnosed with Type 2 Diabetes Mellitus (T2DM) within the U.S. population. Anemia is one of the most common and prevalent…

Scientific Questions Being Studied

The project aims to investigate and assess the prevalence of anemia and associated factors among female adults and elderly individuals diagnosed with Type 2 Diabetes Mellitus (T2DM) within the U.S. population. Anemia is one of the most common and prevalent blood-related disorders that occur in patients with diabetes and it can exert adverse effects on the progression and development of other diabetes-related complications. The outcomes of this project will establish potential associations between several factors and the burden of anemia among diabetic patients. This will help promote health equity among different demographic and clinical subgroups provide endorsement and potential guidance to scientists and clinical management and optimize patient care in the U.S. population.

Project Purpose(s)

  • Population Health

Scientific Approaches

We will use the All of Us study, controlled tired data. Participants with or without anemia will be figured out based on WHO cutoffs. The participants will be estimated across all races, ethnicities, age groups, geographical regions, education levels, and income levels. For continuous variables, the medians with IQR and for categorical n(%) will be summarized. To compare the characteristics of variables between the two groups, a normality test will be reported The prevalence of each group of categorical variables will be estimated and compared by the Chi-square test. Outliers will be excluded according to WHO cutoffs or any biological implausibilities. The univariate and multivariable logistic regression analysis will be applied to compute odds ratios with 95% confidence intervals for potential associated factors with anemia. For univariate, The variables with p-values <0.20 & <0.05 are statistically significant for univariate & multivariate respectively.

Anticipated Findings

This study aims to explore the need for regular anemia screening in all T2DM patients, especially those with risk factors, to enable early detection and management, thus improving overall care quality. By elucidating the potential factors associated with anemia in T2DM patients, the study would enhance our understanding of the pathophysiology and risk factors for anemia in the U.S. population. Identifying demographic, clinical, and lifestyle factors related to anemia will facilitate risk stratification, allowing healthcare providers to prioritize interventions for those at greatest risk. The results may help to improve current knowledge and future research. Overall, the anticipated findings from the study have the potential to significantly contribute to the scientific understanding and clinical management of anemia in individuals with T2DM, ultimately leading to improved healthcare outcomes for the U.S. population.

Demographic Categories of Interest

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

Data Set Used

Controlled Tier

Research Team

Owner:

TAN: Diabetes Type 2: Metformin vs Alternatives

In this study, I want to know the relationship between social determinants of health and prescription of diabetes medication.

Scientific Questions Being Studied

In this study, I want to know the relationship between social determinants of health and prescription of diabetes medication.

Project Purpose(s)

  • Disease Focused Research (Diabetes)
  • Population Health

Scientific Approaches

Main health outcome: prescription of diabetes medication
Research method: secondary analysis
Predictor variables: Social determinants of health

Anticipated Findings

In this study, I want to quantify the relationship between social determinants of health and various diabetes medications.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth
  • Geography
  • Access to Care

Data Set Used

Registered Tier

Research Team

Owner:

Neighborhood environment and diabetes

Diabetes stands as one of the most prevalent metabolic diseases, exerting a disproportionate impact on both sex and racial/ethnic minority populations. While numerous studies have investigated the correlation between social determinants of health and diabetes, a significant gap persists in…

Scientific Questions Being Studied

Diabetes stands as one of the most prevalent metabolic diseases, exerting a disproportionate impact on both sex and racial/ethnic minority populations. While numerous studies have investigated the correlation between social determinants of health and diabetes, a significant gap persists in our comprehension of the most pertinent factors within the intricate landscape of social determinants of health datasets related to diabetes.

1) What are the primary social determinants of health factors associated with diabetes across diverse populations?

2) How do these factors vary in their impact on diabetes within specific sex and race/ethnicity groups?

By addressing these pivotal questions, our research endeavors to refine our understanding of the complex relationship between social determinants of health and diabetes outcomes.

Project Purpose(s)

  • Population Health
  • Social / Behavioral

Scientific Approaches

To address this gap, there is a critical need for sophisticated computational modeling, such as machine learning (ML) algorithms capable of handling high-dimensional data in large datasets, to account for social determinants of health factors in diabetes. We will use supervised ML approaches to examine the associations of individual health factors and neighborhood factors in relation to Diabetes . The supervised ML approach is appropriate because it seeks patterns in the training data and uses that information to make predictions for unseen data without relying on strict model distributions. Four different tree-based machine learning algorithms including: 1) Bayesian Additive Regression Trees, 2) Decision Tree, 3) Random tree, and 4) Gradient Boosting Machines (gbm) will be used to compare their performance in predicting the risk of Diabetes. The root mean squared error and sensitivity analysis will serve as primary indicators of predictive accuracy.

Anticipated Findings

The anticipated outcomes of our study hold the promise of making significant strides in comprehending and mitigating health disparities among minority populations, with a specific focus on predicting the risk of diabetes. We envisage that the development of a predictive framework will play a pivotal role in identifying the key determinants of diabetes.

We anticipate two distinct outcomes from our research: Firstly, we aim to achieve a comprehensive understanding of the intricate relationship between neighborhood environmental factors and diabetes. This understanding will contribute substantially to the refinement of targeted interventions and strategies for the prediction of race/ethnicity- and sex-specific risks associated with diabetes. Secondly, our study aims to pinpoint the key determinants that influence race/ethnicity- and sex-specific disparities in the realm of diabetes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Yangyang Deng - Research Fellow, National Institutes of Health (NIH)

Duplicate of Type 2 diabetes risk prediction

The prevalence of type 2 diabetes (T2D), a highly polygenic disease, has been progressively increasing to epidemic proportions. We are constructing risk prediction tools for T2D and complications that could help guide clinicians implement personalized screening and management approaches to…

Scientific Questions Being Studied

The prevalence of type 2 diabetes (T2D), a highly polygenic disease, has been progressively increasing to epidemic proportions. We are constructing risk prediction tools for T2D and complications that could help guide clinicians implement personalized screening and management approaches to prevent T2D and complications. The integration of polygenic risk scores (PRS) into risk prediction models enhances the performance of these models. Yet the T2D prediction models that consider both clinical risks and T2D PRS fail to account for the underlying heterogeneous pathophysiology of T2D.
We hypothesize that classification models, trained on a large data set and validated on a diverse group of patients, that exhaust the genetic risk for the pathologies involved in T2D development along with the clinical and environmental risk factors will provide more accurate predictions and guide individualized interventions to prevent both T2D and its multiple complications.

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus)

Scientific Approaches

Our hypothesis will be addressed in two specific aims:
Aim 1. To develop a classification model for predicting T2D based on genetic, clinical, and lifestyle risk factors.
After identifying individuals with T2D as well as all relevant clinical and lifestyle risk factors and calculating the PRS for T2D and relevant risk factors, we will use a decision-tree-based ensemble algorithm for classification. We will then select the top features associated with the disease and use them to build a simplified prediction model.
Aim 2. To develop classification models for predicting T2D complications based on genetic, clinical, and lifestyle risk factors.
We will identify people with T2D microvascular (nephropathy, neuropathy, retinopathy) and macrovascular (coronary artery disease, cerebrovascular disease, peripheral artery disease) complications. Multiple PRS for risk factors of each complication, T2D PRS, and the targeted complication PRS will be integrated into the classification model.

Anticipated Findings

We anticipate that this study will improve risk prediction by taking account of the genetic risks of the underlying pathologies involved in the development of T2D and complications as well as the clinical and lifestyle risk factors. We except the classification models for T2D and complication to achieve high predictability in diverse populations. We expect to determine the key risk factors for T2D complications and use them to build simplified focused classification models that will help clinicians make person-centered screening and management decisions to prevent complications.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Shang-Fu Chen - Graduate Trainee, Scripps Research
  • Aidan Cardall - Undergraduate Student, Brigham Young University
  • Aidan Cardall - Undergraduate Student, Scripps Research
  • Justin Wang - Undergraduate Student, Scripps Research

Black Bodies Matter - Diabetes, PAD, Limb Loss

Aim 1: What are the factors influencing early onset (before age 45), co-morbid peripheral artery disease in Black and White patients with diabetes? Aim 2: What is the relationship between the screening practices and limb amputation in patients with diabetes…

Scientific Questions Being Studied

Aim 1: What are the factors influencing early onset (before age 45), co-morbid peripheral artery disease in Black and White patients with diabetes?
Aim 2: What is the relationship between the screening practices and limb amputation in patients with diabetes and concomitant peripheral artery disease?
Aim 3: What factors influence practice decisions to amputate a lower limb in Black and nonblack patients in the United States?
Rationale: Evidence suggests that Black patients have a higher prevalence of asymptomatic PAD and are less aware of symptoms. This issue collectively puts them at increased risk for a delay in care. Black persons diagnosed with chronic kidney disease in midlife ages (45-64) while white persons were diagnosed with kidney disease in later life (65-74), partially attributable to better access to control diabetes. It is anticipated that this will also hold true for PAD.

Project Purpose(s)

  • Population Health
  • Other Purpose (This study aims to understand cultural, environmental, and decision- making factors that contribute to disparities in diagnosis and treatment for PAD with diabetes and subsequent limb loss.)

Scientific Approaches

Study Design: The proposed observational cohort study using the All of Us (AoU) Researcher Workbench.

Sampling Frame: Data from 2018 through the most currently available date at the time of study initiation.
Inclusion Criteria: Participants with a diagnosis of Type II diabetes, at least 18 years of age at the time of agreement to participate in the AoU initiative.
Exclusion Criteria: Any record without a documentation of race
Estimated Sample Size and Power calculation: This is an observational study using an established database. Our previous research using this data source found an estimated 202,000 persons with Type

Type II diabetes in AoU Data Workbench. Based on CDC reporting,6 9% (an estimated 18,180 of the AoU records), of persons with Type II diabetes also will have PAD, and of these 4.6% (an estimated 836 of the AoU records) will possibly have a limb amputation. These are adequately large numbers to support statistical analyses of the proposed hypotheses.

Anticipated Findings

H1A: A decrease in the documentation of screening for PAD will be associated with a higher rate of limb amputations among Black patients compared to other racial and ethnic groups.
H1B: A decrease in the documentation of screening for PAD will be associated with a higher rate of limb amputations in Black patients with diabetes less than 45 years of age compared to other racial and ethnic groups.
H2: An increase in the documentation of vascular treatment services will be associated with a decrease in limb amputations at all stages of diabetes with concomitant PAD.
H3: An increase in the documentation of screening for PAD using an ankle brachial index (ABI) will be associated with a decrease in rate of limb amputations.
H4: An increase in documentation of adherence to the American Heart Association Clinical Practice Guidelines for critical limb ischemia will be associated with a decrease in the rate of limb amputations.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Geography

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • Xiao Zhang - Project Personnel, Xavier University of Louisiana
  • Hongyan Xu - Mid-career Tenured Researcher, Augusta University

APoE vs Diabetes Group Project

The APOE gene has long been associated with Alzheimer's Disease, dementia, and other neurodegenerative diseases. However, the apoE2 allele of this gene has been heavily correlated with high triglyceride levels and is the known cause of a genetic disorder called…

Scientific Questions Being Studied

The APOE gene has long been associated with Alzheimer's Disease, dementia, and other neurodegenerative diseases. However, the apoE2 allele of this gene has been heavily correlated with high triglyceride levels and is the known cause of a genetic disorder called type III hyperlipoproteinemia. Does the presence of APOE mutations significantly influence the development, progression, and management of diabetes mellitus across different populations? This will be a comprehensive investigation of the association between APOE gene variants and diabetes risk, glycemic control, insulin resistance, and complications of diabetes. The findings of this investigation could help to predict the risk of diabetes mellitus in people who carry the APOE mutations.

Project Purpose(s)

  • Educational

Scientific Approaches

We will be using genomics data, surveys and other data available through All of Us. We will also be using the R programming language to make this data presentable and understandable.

Anticipated Findings

The anticipated findings of this research are that APOE mutations contribute significantly to diabetes mellitus risk. There has been a lot of research on the effect of APOE mutations on neurodegenerative diseases, but there has been little to no research on the effect of APOE mutations on diabetes. This study aims to change that.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

APoE vs Diabetes Group Project

The APOE gene has long been associated with Alzheimer's Disease, dementia, and other neurodegenerative diseases. However, the apoE2 allele of this gene has been heavily correlated with high triglyceride levels and is the known cause of a genetic disorder called…

Scientific Questions Being Studied

The APOE gene has long been associated with Alzheimer's Disease, dementia, and other neurodegenerative diseases. However, the apoE2 allele of this gene has been heavily correlated with high triglyceride levels and is the known cause of a genetic disorder called type III hyperlipoproteinemia. Does the presence of APOE mutations significantly influence the development, progression, and management of diabetes mellitus across different populations? This will be a comprehensive investigation of the association between APOE gene variants and diabetes risk, glycemic control, insulin resistance, and complications of diabetes. The findings of this investigation could help to predict the risk of diabetes mellitus in people who carry the APOE mutations.

Project Purpose(s)

  • Educational

Scientific Approaches

We will be using genomics data, surveys and other data available through All of Us. We will also be using the R programming language to make this data presentable and understandable.

Anticipated Findings

The anticipated findings of this research are that APOE mutations contribute significantly to diabetes mellitus risk. There has been a lot of research on the effect of APOE mutations on neurodegenerative diseases, but there has been little to no research on the effect of APOE mutations on diabetes. This study aims to change that.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Type2Diabetes-RareVariantBurden

I want to explore genes and variants that are impacting type 2 diabetes risk between populations . This can better help us understand Type 2 diabetes biology and how it manifest between population groups.

Scientific Questions Being Studied

I want to explore genes and variants that are impacting type 2 diabetes risk between populations . This can better help us understand Type 2 diabetes biology and how it manifest between population groups.

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus)
  • Population Health
  • Ancestry

Scientific Approaches

What genes are associated with Type 2 diabetes when restricting to specific groups of variants (e.g., missense variants and pLoFs)? I will be using Exome data to perform exome-wide association analysis as well as gene-set based burden analysis.

Anticipated Findings

We want to see if there is a burden of different types of variants associated with type 2 diabetes risk. Additionally, is the burden different between population groups in the All of Us Research Program?

Demographic Categories of Interest

  • Race / Ethnicity
  • Age

Data Set Used

Controlled Tier

Research Team

Owner:

  • Shivam Sharma - Graduate Trainee, Georgia Institute of Technology

The effectiveness of the medications for Type 2 diabetes

The primary goal is to understand how different medications impact blood glucose level, manage symptoms, and improve the overall health of patients with diabetes.

Scientific Questions Being Studied

The primary goal is to understand how different medications impact blood glucose level, manage symptoms, and improve the overall health of patients with diabetes.

Project Purpose(s)

  • Educational

Scientific Approaches

The python will be used for our study. Python, featuring libraries like pandas and NumPy, managed initial data pre-processing. We will also use excel to manage the dataset.

Anticipated Findings

Using the HbA1c evaluates the effectiveness of the diabetics' medications. The findings can gain a comprehensive understanding of the effectiveness of diabetic medications.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age

Data Set Used

Registered Tier

Research Team

Owner:

  • YALIN LI - Graduate Trainee, George Mason University

Collaborators:

  • SREE SAI SREEVANI GONGALREDDY - Graduate Trainee, George Mason University
  • niharika sarraf - Graduate Trainee, George Mason University
  • Bhargava Cherukuri - Graduate Trainee, George Mason University

Duplicate of Phenotype - Type 2 Diabetes (v7)

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research, using the Controlled Tier Curated Data Repository (CDR).

Scientific Questions Being Studied

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research, using the Controlled Tier Curated Data Repository (CDR).

Project Purpose(s)

  • Educational
  • Methods Development
  • Other Purpose (This is an All of Us Phenotype Library Workspace created by the Researcher Workbench Support team. It is meant to demonstrate the implementation of key phenotype algorithms within the All of Us Research Program cohort, using the Controlled Tier Curated Data Repository (CDR).)

Scientific Approaches

Controlled-tier All of Us cohort data; Jupyter Notebooks, Cohort Builder, Concept Set Selector, Dataset Selector

Anticipated Findings

By reading and running the Notebooks in this Phenotype Library Workspace, researchers can implement the following phenotype algorithms: Jennifer Pacheco and Will Thompson. Northwestern University. Type 2 Diabetes Mellitus. PheKB; 2012 Available from: https://phekb.org/phenotype/18

This is for a demo for educational purposes of learning how to use Workbench

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Diabetes_TSU

As part of Fisk-TSU All of Us team, we will investigate how race/ethnic group, gender, and other factors would influence prevalence, mortality, and other aspects related to diabetes.

Scientific Questions Being Studied

As part of Fisk-TSU All of Us team, we will investigate how race/ethnic group, gender, and other factors would influence prevalence, mortality, and other aspects related to diabetes.

Project Purpose(s)

  • Educational

Scientific Approaches

We will conduct literature review, collect data using the workbench, and conduct data analysis, and present the results.

Anticipated Findings

We expected to see differences in diabetes prevalence, mortality etc among different race/ethnicity groups. Hope these results would improve our understanding of the distribution of the disease and improve health.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth
  • Sexual Orientation
  • Geography

Data Set Used

Registered Tier

Research Team

Owner:

  • Dafeng Hui - Late Career Tenured Researcher, Fisk University

Collaborators:

  • Takara Pierce - Undergraduate Student, Tennessee State University
  • Stefanee Tillman - Project Personnel, All of Us Researcher Academy/RTI International
  • Sarena Noel - Undergraduate Student, Tennessee State University
  • Javan Carter - Research Associate, All of Us Researcher Academy/RTI International

Genetic-risk factors for type 2 diabetes

Type 2 diabetes mellitus (T2DM) is a chronic disease that affects millions of people worldwide. T2DM is associated with a variety of major health complications and affects individuals of different ethnicities and genetic ancestries. The onset and progression of T2DM…

Scientific Questions Being Studied

Type 2 diabetes mellitus (T2DM) is a chronic disease that affects millions of people worldwide. T2DM is associated with a variety of major health complications and affects individuals of different ethnicities and genetic ancestries. The onset and progression of T2DM can be significantly influenced by personal behaviors, environmental factors and genetic variation. In this study, we aim to identify the genetic risk factors associated with T2DM for people with different genetic ancestries and ethnicities. Identifying genetic risk factors for T2DM is important because they can help us better understand the underlying mechanisms of the disease and potentially develop new treatments and prevention strategies to advance precision medicine. The results would help to develop personalized plans that are tailored to stratify T2DM risk depending on the specific ancestry and ethnicity of the patient.

Project Purpose(s)

  • Disease Focused Research (Type 2 diabetes mellitus )
  • Ancestry

Scientific Approaches

Our lab has developed gene-constrained methods for comparative analysis of genetic data between patient and non-patient populations of diseases that are affected by multiple genes. These new methods are complementary to the well-known Genome-Wide Association Study (GWAS) and can identify disease-associated genetic variants that are often missed by GWAS. We have used one such method successfully, employing the relatively small dataset that was then available, to identify dozens of previously unknown genes that are potentially associated with T2DM. We plan to apply this method to the larger datasets now available in All of US to analyze people with T2DM to those without in a way that accounts for both ancestry and gender.

Anticipated Findings

Using our newly developed genetic analytical methods and the larger datasets available in All of US, we expect to identify more genes that are associated with T2DM. The analyses in this workspace will take both ancestry and gender into consideration to sensitize the detection of relevant genes and to ensure that the risk genes we identify are more accurate, relevant, and specific to the development of T2DM. Our findings will help the scientific community and the public to better understand the genetic contributions to T2DM, which could lead to the development of more precise and personalized management plans for T2DM based on individualized genetic risk.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Jianhua Zhang - Senior Researcher, National Institute of Diabetes and Digestive and Kidney Diseases (NIH - NIDDK)

Collaborators:

  • Harrison McNabb - Research Fellow, National Institute of Diabetes and Digestive and Kidney Diseases (NIH - NIDDK)
  • Dayo Shittu - Project Personnel, National Human Genome Research Institute (NIH-NHGRI)

Gestational Diabetes Mellitus in Pregnant Women

RQ1: How do health literacy needs differ by race/ethnicity in pregnant women with and without gestational diabetes? RQ2: How do health literacy needs differ by race/ethnicity and income in pregnant women with and without gestational diabetes? RQ3: How do health…

Scientific Questions Being Studied

RQ1: How do health literacy needs differ by race/ethnicity in pregnant women with and without gestational diabetes?
RQ2: How do health literacy needs differ by race/ethnicity and income in pregnant women with and without gestational diabetes?
RQ3: How do health literacy needs differ by race/ethnicity and education level in pregnant women with and without gestational diabetes?

We identified four questions that address health literacy needs:
1) How often do you have someone help you read health-related materials?;
2) How often do you have problems learning about your medical condition because of difficulty understanding written information?;
3) How often did your doctors or health care providers tell or give you information about your health and health care that was easy to understand?; and
4) How confident are you filling out medical forms by yourself?

Project Purpose(s)

  • Disease Focused Research (Gestational diabetes )
  • Social / Behavioral

Scientific Approaches

We will use the the survey data set to answer questions about gestational diabetes status, health literacy related items, income and education . We plan on using the R program to answer these questions.

Anticipated Findings

We hope these answers will improve the experiences of women with and without GMD improve health literacy efforts for these women. Findings can contribute to the patient education efforts on GMD to pregnant women and improving communication.

Demographic Categories of Interest

  • Race / Ethnicity
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

  • Julie Volkman - Mid-career Tenured Researcher, Bryant University
  • Ben Gerber - Late Career Tenured Researcher, University of Massachusetts Medical School
  • Alicia Lamere - Early Career Tenure-track Researcher, Stonehill College

Duplicate of Phenotype - Type 2 Diabetes (v7)

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research, using the Controlled Tier Curated Data Repository (CDR).

Scientific Questions Being Studied

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research, using the Controlled Tier Curated Data Repository (CDR).

Project Purpose(s)

  • Educational
  • Methods Development
  • Other Purpose (This is an All of Us Phenotype Library Workspace created by the Researcher Workbench Support team. It is meant to demonstrate the implementation of key phenotype algorithms within the All of Us Research Program cohort, using the Controlled Tier Curated Data Repository (CDR).)

Scientific Approaches

Controlled-tier All of Us cohort data; Jupyter Notebooks, Cohort Builder, Concept Set Selector, Dataset Selector

Anticipated Findings

By reading and running the Notebooks in this Phenotype Library Workspace, researchers can implement the following phenotype algorithms: Jennifer Pacheco and Will Thompson. Northwestern University. Type 2 Diabetes Mellitus. PheKB; 2012 Available from: https://phekb.org/phenotype/18

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Diabetes Mellitus and Obesity among Hisp/Lat, NHW, NHB, Non-Hisp Asian

The overall goal of this project is to examine whether there is evidence of race/ethnic differences in the prevalence of diabetes mellitus and obesity within the All of Us Research Project (AoURP) cohort. We will address the following aims: •…

Scientific Questions Being Studied

The overall goal of this project is to examine whether there is evidence of race/ethnic differences in the prevalence of diabetes mellitus and obesity within the All of Us Research Project (AoURP) cohort. We will address the following aims:
• Specific Aim #1. To determine whether Latinos have higher prevalence of gender stratified age-adjusted diabetes and obesity (overall) versus non-Hispanic whites (NHW), non-Hispanic blacks (NHB), and non-Hispanic Asians in the cohort.
• Specific Aim #2: To extent possible examine differences by Latino subgroups and among foreign born versus US born Latinos

Project Purpose(s)

  • Disease Focused Research (Diabetes mellitus and obesity)
  • Population Health

Scientific Approaches

Study population. All of Us Research Project core participants. We will examine data from different data sources including electronic health records (EHR) and participant provided information (PPI) and physical measurements.

Main outcome variables: we will work with the DRC Research Support Team to obtain support for their existing classification scheme for common complex diseases which in this project would include Diabetes mellitus and obesity. For the definition of diseases we will use EHR data to preserve very objective outcomes, excluding for now survey data.

Statistical analysis
We will present all data stratified by gender and age adjusted using direct standardization. BMI categories would be <25, 25-30, 30-35 and >35). For diabetes AIC data will be categorized (AIC <7, AIC 7-9 and AIC > 9).

Anticipated Findings

To examine race/ethnic differences in the prevalence of diabetes mellitus and obesity within the AoURP cohort. We aim to compare the prevalence rates among different racial/ethnic groups, such as Latinos, Blacks, Asians, and Non-Hispanic Whites (NHWs).

Prior research, such as the Study of Latinos (SOL) – which was the largest study of Latinos with 16,000 participants – primarily focused on health disparities within the Latino population. However, this study had a limitation as it only included Latinos, lacking comparative data on non-Latinos. With the All of Us Research Project (AoURP) having over 40,000 Latino core participants and more than 160,000 non-Latinos, it provides a comprehensive platform to compare and contrast the prevalence of diabetes mellitus and obesity across diverse racial/ethnic groups.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Registered Tier

Research Team

Owner:

Cardiovascular Risk Factors Among Diabetes Patients

Are there specific genetic variants associated with increased risk of developing cardiovascular disease among patients with diabetes? What is the prevalence of cardiovascular genetic risk factors among the diabetic Hispanic population? Studying genetic variations linked to higher cardiovascular disease risk…

Scientific Questions Being Studied

Are there specific genetic variants associated with increased risk of developing cardiovascular disease among patients with diabetes?
What is the prevalence of cardiovascular genetic risk factors among the diabetic Hispanic population?

Studying genetic variations linked to higher cardiovascular disease risk in diabetes individuals, especially in the Hispanic community, is essential for progressing personalize care and improving health outcomes. Identifying distinct genetic risk factors for cardiovascular disease in people with diabetes is the first step in developing personalized prevention and treatment approaches, especially in minority groups like US Hispanics. Further, a better understanding genetic predisposition may improve our biological understanding of cardiovascular disease in diabetics and facilitate specific therapies to lessen the burden of cardiovascular problems in this at-risk population.

Project Purpose(s)

  • Disease Focused Research (diabetes mellitus)
  • Ancestry

Scientific Approaches

Cohort selection: Create cohort of diabetic patients in the All of Us program. We will then identify patients who develop the outcome of interest (the presence of cardiovascular disease [CVD]). Data collection: Data will be collected from the All of Us database. The data should include demographic details, clinical parameters, individuals' genetics and outcomes related to diabetes and CVD.

Generating Descriptive statistics of the population:
Statistical Analysis: The use of statistical software (eg. Python) to generate descriptive statistics for the diabetes cohort. It will include means and standard deviations for continuous variables and frequencies for categorical variables.

Candidate gene study to identify if candidate SNPs (rs6923761, rs57922, rs9299870) are associated with risk of CVD in diabetes patients. This will involve the association of these SNPs within cardiovascular disease among diabetic patients.

Anticipated Findings

Anticipated findings: Specific genetic variations, interacting with the glucagon-like peptide-1 (GLP-1) signaling system, provide a complex risk factor for cardiovascular disease in people with diabetes. We anticipate that genetic variations in the GLP-1 signaling system may impact the efficacy of GLP-1 receptor agonists by affecting insulin production, glucose metabolism, and atherosclerosis as well as overall cardiovascular risk. The study's findings are anticipated to improve our comprehension of why specific diabetes patients are more susceptible to cardiovascular problems and how their reaction to therapy can differ depending on genetic factors. This method not only seeks to enhance scientific understanding but also strives to tackle healthcare inequalities and enhance public health results by customizing interventions to suit the unique requirements of individual patients, according to their genetic makeup.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Valeria Miranda - Graduate Trainee, University of Puerto Rico Medical Sciences
  • Kyle Melin - Mid-career Tenured Researcher, University of Puerto Rico Medical Sciences
  • Jonathan Hernandez-Agosto - Early Career Tenure-track Researcher, University of Puerto Rico Medical Sciences
  • Bianca Torres - Early Career Tenure-track Researcher, University of Puerto Rico Medical Sciences

Structural variants in diabetes

Genetic association studies have identified hundreds of genetic susceptibility alleles for type 1 diabetes (T1D) and type 2 diabetes (T2D). However, genetic association and fine mapping have primarily focused on genetic variation that can be genotyped or imputed using SNP…

Scientific Questions Being Studied

Genetic association studies have identified hundreds of genetic susceptibility alleles for type 1 diabetes (T1D) and type 2 diabetes (T2D). However, genetic association and fine mapping have primarily focused on genetic variation that can be genotyped or imputed using SNP arrays or short-read sequencing, namely single nucleotide variants (SNV) and small indels. Including the full spectrum of genetic variation in diabetes association analyses may reveal novel disease mechanisms at known diabetes loci. Here, I hope to use 1000 PacBio long-read genomes available through All of Us to investigate structural variants (SV) underlying common allele associations with diabetes.

Project Purpose(s)

  • Disease Focused Research (diabetes mellitus)
  • Ancestry

Scientific Approaches

I will use published algorithms to define All of Us cohorts based on EHR and survey response data (Szczerbinski et al., medRxiv 2023): (i) T1D cases, (ii) T2D cases, and (iii) controls with no history of diabetes. I will impute SV variants in these cohorts in diabetes regions. I will then perform discovery and fine mapping for diabetes associations using SNV and imputed SV genotypes.

Additionally, to explore potential evidence of somatic instability at diabetes associated SVs, we will investigate allelic heterogeneity of long reads overlapping these regions. Specifically, we will look for instances where reads demonstrate unusual allelic fractions (e.g., more than 2 alleles are observed in heterozygous individuals, or small fractions of alternative alleles are observed in homozygous individuals).

Anticipated Findings

Identifying causal structural variants underlying common allele association with diabetes will inform mechanistic studies of causal pathways in diabetes and may provide novel insights about disease etiology.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Cassie Robertson - Research Fellow, National Human Genome Research Institute (NIH - NHGRI)

ML Diabetes Health Disparities

We are looking at investigating the social | economic | genetic | and geographic causes of health disparities, with an exploratory study using diabetes. This part of the research taking place in the Jordan Lab at Georgia Tech will also…

Scientific Questions Being Studied

We are looking at investigating the social | economic | genetic | and geographic causes of health disparities, with an exploratory study using diabetes. This part of the research taking place in the Jordan Lab at Georgia Tech will also be used to introduce masters students to advanced data analysis techniques.

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus)
  • Population Health
  • Educational
  • Ancestry

Scientific Approaches

We intend to use new metrics that combine geographic and socioeconomic data with a genetic basis for ancestry to stratify the occurence of diabetes in populations, then use advanced machine learning techniques to determine the relative occurence of diabetes within those populations.

Anticipated Findings

This research is as much a validation study of the metrics proposed by others in the lab group as it is an exploratory study. We will first seek to see if the conclusions rendered from UK biobank are matched.

Demographic Categories of Interest

  • Race / Ethnicity
  • Geography
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

  • Sonali Gupta - Research Assistant, National Institutes of Health (NIH)
  • Onyinye Chukka - Graduate Trainee, Georgia Institute of Technology

Type2Diabetes

I want to explore genes and variants that are impacting type 2 diabetes risk. This can better help us understand Type 2 diabetes biology.

Scientific Questions Being Studied

I want to explore genes and variants that are impacting type 2 diabetes risk. This can better help us understand Type 2 diabetes biology.

Project Purpose(s)

  • Population Health
  • Educational

Scientific Approaches

What genes are associated with Type 2 diabetes when restricting to specific groups of variants (e.g., missense variants and pLoFs)?

Anticipated Findings

We want to see if there is a burden of different types of variants associated with type 2 diabetes risk.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Shivam Sharma - Graduate Trainee, Georgia Institute of Technology

Collaborators:

  • Courtney Astore - Graduate Trainee, Georgia Institute of Technology

Molecular factors of statin-induced new-onset diabetes (NOD) V6

We are currently exploring the available data to obtain demographic data and characteristics. We plan to focus on questions such as: 1. What is the association between type 2 diabetes (T2D) polygenic risk (PRS) and statin-induced new onset diabetes (NOD).…

Scientific Questions Being Studied

We are currently exploring the available data to obtain demographic data and characteristics. We plan to focus on questions such as:
1. What is the association between type 2 diabetes (T2D) polygenic risk (PRS) and statin-induced new onset diabetes (NOD).
2. What are the relevant contibutions of multiple T2D-relevant molecular pathways to the association between T2D polygenic risk and statin-induced NOD.

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus)
  • Drug Development
  • Ancestry

Scientific Approaches

We plan to use the All of Us dataset. We will generate a robust statin-induced NOD phenotype from adherent statin users and propensity-matched nonusers of a large electronic health record-linked genomic biobank. We will then characterize the polygenic risk of T2D in individuals from this cohort using the summary statistics of prior genome-wide association studies. We will stratify participants by polygenic risk and determine the relative risk of statin-induced NOD within each strata. We will separate individual variants from our T2D polygenic risk score into partitions based on biological processes (e.g., inflammation, pancreatic beta-cell function). We will stratify participants by polygenic risk for each partition before determining the relative risk of statin-induced NOD within each partition strata.

Anticipated Findings

Findings from the proposed studies will provide additional mechanistic insight into statin-induced NOD that will ultimately (1) inform the development of novel strategies to prevent this side effect and (2) identify molecular biomarkers for the improved optimization of statin benefit-risk assessment in patients eligible for therapy.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

  • Michael Douglas - Project Personnel, University of California, San Francisco

Duplicate of Molecular factors of statin-induced new-onset diabetes (NOD) V7

We are currently exploring the available data to obtain demographic data and characteristics. We plan to focus on questions such as: 1. What is the association between type 2 diabetes (T2D) polygenic risk (PRS) and statin-induced new onset diabetes (NOD).…

Scientific Questions Being Studied

We are currently exploring the available data to obtain demographic data and characteristics. We plan to focus on questions such as:
1. What is the association between type 2 diabetes (T2D) polygenic risk (PRS) and statin-induced new onset diabetes (NOD).
2. What are the relevant contibutions of multiple T2D-relevant molecular pathways to the association between T2D polygenic risk and statin-induced NOD.

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus)
  • Drug Development
  • Ancestry

Scientific Approaches

We plan to use the All of Us dataset. We will generate a robust statin-induced NOD phenotype from adherent statin users and propensity-matched nonusers of a large electronic health record-linked genomic biobank. We will then characterize the polygenic risk of T2D in individuals from this cohort using the summary statistics of prior genome-wide association studies. We will stratify participants by polygenic risk and determine the relative risk of statin-induced NOD within each strata. We will separate individual variants from our T2D polygenic risk score into partitions based on biological processes (e.g., inflammation, pancreatic beta-cell function). We will stratify participants by polygenic risk for each partition before determining the relative risk of statin-induced NOD within each partition strata.

Anticipated Findings

Findings from the proposed studies will provide additional mechanistic insight into statin-induced NOD that will ultimately (1) inform the development of novel strategies to prevent this side effect and (2) identify molecular biomarkers for the improved optimization of statin benefit-risk assessment in patients eligible for therapy.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

  • Michael Douglas - Project Personnel, University of California, San Francisco

Duplicate of Phenotype - Type 2 Diabetes (v7)

This pilot project seeks to determine if there is an association between sleep disturbance and type 2 diabetes mellitus in African Americans.

Scientific Questions Being Studied

This pilot project seeks to determine if there is an association between sleep disturbance and type 2 diabetes mellitus in African Americans.

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus)
  • Population Health

Scientific Approaches

Controlled-tier All of Us cohort data; Jupyter Notebooks, Cohort Builder, Concept Set Selector, Dataset Selector

Anticipated Findings

This pilot study will support previous findings of an association between sleep disturbance and development of type 2 diabetes mellitus in African American individuals. These findings have the capacity to support existing hypotheses using a new database.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

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