Research Projects Directory

Maternal Mortality and Nutritional Abnormalities

We are doing a systematic review research examining the role of nutritional status in increasing the risk factors for maternal complications and mortality in Non-Hispanic Black women. PICOT: What are the nutrient deficiencies & anthropometric abnormalities that contribute to maternal…

Scientific Questions Being Studied

We are doing a systematic review research examining the role of nutritional status in increasing the risk factors for maternal complications and mortality in Non-Hispanic Black women.

PICOT: What are the nutrient deficiencies & anthropometric abnormalities that contribute to maternal complications and mortality in Non-Hispanic Black women compared to White women?
metric abnormalities that contribute to maternal complications and mortality in Non-Hispanic Black women compared to White women?

Project Purpose(s)

Disease Focused Research (hemorrhagic disorders, cardiomyopathy and nutritional disorders in pregnancy )

Scientific Approaches

We are doing a systematic review and want to include All of Data to support our research. We are currently finalizing our PRISMA problem statement and how we will approach our findings.

Anticipated Findings

Links between prenatal and antenatal nutritional disorders and common causes of maternal mortality such as hemorrhage, cardiomyopathy and infection

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Brianna Hector - Graduate Trainee, Philadelphia College of Osteopathic Medicine

v7 of Prevalence of carriers of inborn errors of metabolism

Inborn errors of metabolism (IEM) are rare diseases that are often recessive. But several large cohort studies revealed metabolic individuality in healthy populations. In other words, one's unique genetic makeup affects the way our body performs metabolism and the disposition…

Scientific Questions Being Studied

Inborn errors of metabolism (IEM) are rare diseases that are often recessive. But several large cohort studies revealed metabolic individuality in healthy populations. In other words, one's unique genetic makeup affects the way our body performs metabolism and the disposition of metabolism-related diseases. Understanding the prevalence and characteristics of metabolic outliers may help us devise precision nutrition strategies that may help improve the health of these individuals. The specific question we would like to investigate in this area using the All of Us data are:
1. The frequency of pathogenic variants in the population
2. The frequency of GWAS metabolism-related variants
3. The demographic characteristics of these predicted metabolic outlier individuals

Project Purpose(s)

Disease Focused Research (inherited metabolic disorder)
Ancestry

Scientific Approaches

We will first perform some exploratory analysis to understand the data structure of the All of Us data. Then, we will prepare a list of pathogenic variants on IEM genes, as well as a list of GWAS variants on IEM genes that associate with related metabolic traits from existing databases and publications. We will evaluate the frequency of these variants in the All of Us data. The demographic characteristics of the variant carriers will be summarized. As many of these variants may be rare but with large effect sizes, we will aggregate data to the gene level instead of the variant level.

Anticipated Findings

We expect this work will give us an estimate of metabolic outlier population size and characteristics for future studies that try to implement precision nutrition.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Ling Cai - Project Personnel, University of Texas Southwestern Medical Center
He Zhang - Research Fellow, University of Texas Southwestern Medical Center

Genomics_env_v7_Prevalence_of_carriers_of_inborn_errors_of_metabolism

Inborn errors of metabolism (IEM) are rare diseases that are often recessive. But several large cohort studies revealed metabolic individuality in healthy populations. In other words, one's unique genetic makeup affects the way our body performs metabolism and the disposition…

Scientific Questions Being Studied

Inborn errors of metabolism (IEM) are rare diseases that are often recessive. But several large cohort studies revealed metabolic individuality in healthy populations. In other words, one's unique genetic makeup affects the way our body performs metabolism and the disposition of metabolism-related diseases. Understanding the prevalence and characteristics of metabolic outliers may help us devise precision nutrition strategies that may help improve the health of these individuals. The specific question we would like to investigate in this area using the All of Us data are:
1. The frequency of pathogenic variants in the population
2. The frequency of GWAS metabolism-related variants
3. The demographic characteristics of these predicted metabolic outlier individuals

Project Purpose(s)

Disease Focused Research (inherited metabolic disorder)
Ancestry

Scientific Approaches

We will first perform some exploratory analysis to understand the data structure of the All of Us data. Then, we will prepare a list of pathogenic variants on IEM genes, as well as a list of GWAS variants on IEM genes that associate with related metabolic traits from existing databases and publications. We will evaluate the frequency of these variants in the All of Us data. The demographic characteristics of the variant carriers will be summarized. As many of these variants may be rare but with large effect sizes, we will aggregate data to the gene level instead of the variant level.

Anticipated Findings

We expect this work will give us an estimate of metabolic outlier population size and characteristics for future studies that try to implement precision nutrition.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Ling Cai - Project Personnel, University of Texas Southwestern Medical Center
He Zhang - Research Fellow, University of Texas Southwestern Medical Center

Duplicate of Demo - Hypertension Prevalence

We are using the All of Us Researcher Workbench interface to answer the question, "Is hypertension prevalence in the All of Us Research Program similar to hypertension prevalence in the 2015–2016 National Health and Nutrition Examination Survey (NHANES) ?". Clinical…

Scientific Questions Being Studied

We are using the All of Us Researcher Workbench interface to answer the question, "Is hypertension prevalence in the All of Us Research Program similar to hypertension prevalence in the 2015–2016 National Health and Nutrition Examination Survey (NHANES) ?". Clinical approaches to understanding and treating hypertension may benefit from the integration of a precision medicine approach that integrates data on environments, social determinants of health, behaviors, and genomic factors that contribute to hypertension risk. Hypertension is a major public health concern and remains a leading risk factor for stroke and cardiovascular disease.

Project Purpose(s)

Other Purpose (This work is an AoU demo project. Demo projects are efforts by the AoU Research Program designed to meet the program goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. As an approved demo project, this work was reviewed and overseen by the AoU Research Program Science Committee and the AoU Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use. )

Scientific Approaches

In this cross-sectional, population-based study, we used All of Us baseline data from patient (age>18) provided information (PPI) surveys and electronic health record (EHR) blood pressure measurements and retrospectively examined the prevalence of hypertension in the EHR cohort using Systemized Nomenclature of Medicine (SNOMED codes and blood pressure medications recorded in the EHR. We used the EHR data (SNOMED codes on 2 distinct dates and at least one hypertension medication) as the primary definition, and then add subjects with elevated systolic or elevated diastolic blood pressure on measurements 2 and 3 from PPI. We extracted each participant’s detailed dates of SNOMED code for essential hypertension from the Researcher Workbench table ‘cb_search_all_events’. We calculated an age-standardized HTN prevalence according to the age distribution of the U.S. Census, using 3 groups (18-39, 40-59, ≥ 60).

Anticipated Findings

The prevalence of hypertension in the All of Us cohort is similar to that of published literature. All of Us age-adjusted HTN prevalence was 27.9% compared to 29.6% in National Health and Nutrition Examination Survey. The All of Us cohort is a growing source of diverse longitudinal data that can be utilized to study hypertension nationwide. The prevalence of hypertension varies in the United States (U.S.) by age, sex, and socioeconomic status. Hypertension can often be treated successfully with medication, and prevented or delayed with lifestyle modifications. Even with these established hypertension intervention and prevention strategies, the prevalence of hypertension continues to be at levels of public health concern. The diversity within All of Us may provide insight into factors relevant to hypertension prevention and treatments in a variety of social and geographic contexts and population strata in the U.S.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Abdul Mannan Khan Minhas - Other, Baylor College of Medicine

Explore Hypertension Data

We are using the All of Us Researcher Workbench interface to answer the question, "Is hypertension prevalence in the All of Us Research Program similar to hypertension prevalence in the 2015–2016 National Health and Nutrition Examination Survey (NHANES) ?". Clinical…

Scientific Questions Being Studied

We are using the All of Us Researcher Workbench interface to answer the question, "Is hypertension prevalence in the All of Us Research Program similar to hypertension prevalence in the 2015–2016 National Health and Nutrition Examination Survey (NHANES) ?". Clinical approaches to understanding and treating hypertension may benefit from the integration of a precision medicine approach that integrates data on environments, social determinants of health, behaviors, and genomic factors that contribute to hypertension risk. Hypertension is a major public health concern and remains a leading risk factor for stroke and cardiovascular disease.

Project Purpose(s)

Other Purpose (This work is an AoU demo project. Demo projects are efforts by the AoU Research Program designed to meet the program goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. As an approved demo project, this work was reviewed and overseen by the AoU Research Program Science Committee and the AoU Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use. )

Scientific Approaches

In this cross-sectional, population-based study, we used All of Us baseline data from patient (age>18) provided information (PPI) surveys and electronic health record (EHR) blood pressure measurements and retrospectively examined the prevalence of hypertension in the EHR cohort using Systemized Nomenclature of Medicine (SNOMED codes and blood pressure medications recorded in the EHR. We used the EHR data (SNOMED codes on 2 distinct dates and at least one hypertension medication) as the primary definition, and then add subjects with elevated systolic or elevated diastolic blood pressure on measurements 2 and 3 from PPI. We extracted each participant’s detailed dates of SNOMED code for essential hypertension from the Researcher Workbench table ‘cb_search_all_events’. We calculated an age-standardized HTN prevalence according to the age distribution of the U.S. Census, using 3 groups (18-39, 40-59, ≥ 60).

Anticipated Findings

The prevalence of hypertension in the All of Us cohort is similar to that of published literature. All of Us age-adjusted HTN prevalence was 27.9% compared to 29.6% in National Health and Nutrition Examination Survey. The All of Us cohort is a growing source of diverse longitudinal data that can be utilized to study hypertension nationwide. The prevalence of hypertension varies in the United States (U.S.) by age, sex, and socioeconomic status. Hypertension can often be treated successfully with medication, and prevented or delayed with lifestyle modifications. Even with these established hypertension intervention and prevention strategies, the prevalence of hypertension continues to be at levels of public health concern. The diversity within All of Us may provide insight into factors relevant to hypertension prevention and treatments in a variety of social and geographic contexts and population strata in the U.S.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Brooke Morris - Graduate Trainee, Baylor University

Duplicate of Demo - Hypertension Prevalence

We are using the All of Us Researcher Workbench interface to answer the question, "Is hypertension prevalence in the All of Us Research Program similar to hypertension prevalence in the 2015–2016 National Health and Nutrition Examination Survey (NHANES) ?". Clinical…

Scientific Questions Being Studied

We are using the All of Us Researcher Workbench interface to answer the question, "Is hypertension prevalence in the All of Us Research Program similar to hypertension prevalence in the 2015–2016 National Health and Nutrition Examination Survey (NHANES) ?". Clinical approaches to understanding and treating hypertension may benefit from the integration of a precision medicine approach that integrates data on environments, social determinants of health, behaviors, and genomic factors that contribute to hypertension risk. Hypertension is a major public health concern and remains a leading risk factor for stroke and cardiovascular disease.

Project Purpose(s)

Other Purpose (This work is an AoU demo project. Demo projects are efforts by the AoU Research Program designed to meet the program goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. As an approved demo project, this work was reviewed and overseen by the AoU Research Program Science Committee and the AoU Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use. )

Scientific Approaches

In this cross-sectional, population-based study, we used All of Us baseline data from patient (age>18) provided information (PPI) surveys and electronic health record (EHR) blood pressure measurements and retrospectively examined the prevalence of hypertension in the EHR cohort using Systemized Nomenclature of Medicine (SNOMED codes and blood pressure medications recorded in the EHR. We used the EHR data (SNOMED codes on 2 distinct dates and at least one hypertension medication) as the primary definition, and then add subjects with elevated systolic or elevated diastolic blood pressure on measurements 2 and 3 from PPI. We extracted each participant’s detailed dates of SNOMED code for essential hypertension from the Researcher Workbench table ‘cb_search_all_events’. We calculated an age-standardized HTN prevalence according to the age distribution of the U.S. Census, using 3 groups (18-39, 40-59, ≥ 60).

Anticipated Findings

The prevalence of hypertension in the All of Us cohort is similar to that of published literature. All of Us age-adjusted HTN prevalence was 27.9% compared to 29.6% in National Health and Nutrition Examination Survey. The All of Us cohort is a growing source of diverse longitudinal data that can be utilized to study hypertension nationwide. The prevalence of hypertension varies in the United States (U.S.) by age, sex, and socioeconomic status. Hypertension can often be treated successfully with medication, and prevented or delayed with lifestyle modifications. Even with these established hypertension intervention and prevention strategies, the prevalence of hypertension continues to be at levels of public health concern. The diversity within All of Us may provide insight into factors relevant to hypertension prevention and treatments in a variety of social and geographic contexts and population strata in the U.S.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Chris Hemme - Project Personnel, University of Rhode Island

Duplicate of Demo - Hypertension Prevalence

We are using the All of Us Researcher Workbench interface to answer the question, "Is hypertension prevalence in the All of Us Research Program similar to hypertension prevalence in the 2015–2016 National Health and Nutrition Examination Survey (NHANES) ?". Clinical…

Scientific Questions Being Studied

We are using the All of Us Researcher Workbench interface to answer the question, "Is hypertension prevalence in the All of Us Research Program similar to hypertension prevalence in the 2015–2016 National Health and Nutrition Examination Survey (NHANES) ?". Clinical approaches to understanding and treating hypertension may benefit from the integration of a precision medicine approach that integrates data on environments, social determinants of health, behaviors, and genomic factors that contribute to hypertension risk. Hypertension is a major public health concern and remains a leading risk factor for stroke and cardiovascular disease.

Project Purpose(s)

Other Purpose (This work is an AoU demo project. Demo projects are efforts by the AoU Research Program designed to meet the program goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. As an approved demo project, this work was reviewed and overseen by the AoU Research Program Science Committee and the AoU Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use. )

Scientific Approaches

In this cross-sectional, population-based study, we used All of Us baseline data from patient (age>18) provided information (PPI) surveys and electronic health record (EHR) blood pressure measurements and retrospectively examined the prevalence of hypertension in the EHR cohort using Systemized Nomenclature of Medicine (SNOMED codes and blood pressure medications recorded in the EHR. We used the EHR data (SNOMED codes on 2 distinct dates and at least one hypertension medication) as the primary definition, and then add subjects with elevated systolic or elevated diastolic blood pressure on measurements 2 and 3 from PPI. We extracted each participant’s detailed dates of SNOMED code for essential hypertension from the Researcher Workbench table ‘cb_search_all_events’. We calculated an age-standardized HTN prevalence according to the age distribution of the U.S. Census, using 3 groups (18-39, 40-59, ≥ 60).

Anticipated Findings

The prevalence of hypertension in the All of Us cohort is similar to that of published literature. All of Us age-adjusted HTN prevalence was 27.9% compared to 29.6% in National Health and Nutrition Examination Survey. The All of Us cohort is a growing source of diverse longitudinal data that can be utilized to study hypertension nationwide. The prevalence of hypertension varies in the United States (U.S.) by age, sex, and socioeconomic status. Hypertension can often be treated successfully with medication, and prevented or delayed with lifestyle modifications. Even with these established hypertension intervention and prevention strategies, the prevalence of hypertension continues to be at levels of public health concern. The diversity within All of Us may provide insight into factors relevant to hypertension prevention and treatments in a variety of social and geographic contexts and population strata in the U.S.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Yue Bao - Graduate Trainee, Worcester Polytechnic Institute

Obesity

Many studies have found a positive association between risk for glaucoma and obesity. Weight loss has been proposed to be potential preventative lifestyle modification for glaucoma, though complicated by nutritional deficiencies. This still will look at the impact of obesity…

Scientific Questions Being Studied

Many studies have found a positive association between risk for glaucoma and obesity. Weight loss has been proposed to be potential preventative lifestyle modification for glaucoma, though complicated by nutritional deficiencies. This still will look at the impact of obesity medications on glaucoma and identify potential modifiable risk factors for glaucoma and understand the impact of pharmacological agents in a population with high prevalence of glaucoma

Project Purpose(s)

Disease Focused Research (glaucoma)
Educational
Drug Development

Scientific Approaches

case control or cross-sectional study with multiple logistics regression analysis, obesity data set, obesity medication dataset, icd-10/icd-9 dataset

Anticipated Findings

Look at the impact of obesity medications on glaucoma and identify potential modifiable risk factors for glaucoma and understand the impact of pharmacological agents in a population with high prevalence of glaucoma

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Jennifer Lee - Graduate Trainee, University of California, Los Angeles

Collaborators:

Ramin Talebi - Graduate Trainee, University of California, Los Angeles

(Duplicate of)^2 Multivariate trajectories of treatment response...

Treatment response in chronic diseases is a multifaceted and heterogeneous process that evolves over time and differs per patient. In practice, it is often defined as a binary outcome at a single point in time, which discards valuable temporal information.…

Scientific Questions Being Studied

Treatment response in chronic diseases is a multifaceted and heterogeneous process that evolves over time and differs per patient. In practice, it is often defined as a binary outcome at a single point in time, which discards valuable temporal information. The goal of this proposed research is to use multivariate temporal data to identify clusters of patients with different therapeutic response trajectories and potentially different optimal treatments for diabetic kidney disease and immune-mediated inflammatory diseases. This work can inform future therapeutic decision-making, trial design, and drug discovery for patients with chronic diseases. This work will also provide a foundation that can be extended to other modalities (metabolome, microbiome, nutrition, wearables) as real-world datasets increase in volume and complexity.

Project Purpose(s)

Disease Focused Research (immune-mediated inflammatory diseases)
Social / Behavioral
Drug Development
Methods Development
Ancestry

Scientific Approaches

By applying unsupervised clustering methods (VaDER, CRLI) to multivariate longitudinal measures (laboratory results, vital signs, physical measurements) from real-world datasets (All of Us, UK Biobank), we will identify common response trajectories to chronic disease therapies grouped by mechanisms of action. Leveraging diverse data (health records, participant surveys, genomics) we will perform quantitative and qualitative assessment of associations between identified trajectory clusters and baseline patient characteristics (demographics, comorbidities, medication use, social determinants of health, genetic variants).

Anticipated Findings

We anticipate the discovery of discrete IMID and DKD patient clusters based on their variable response to treatment across a number of longitudinal clinical measures. We will characterize our cluster results from two deep learning-based methods (VaDER, CRLI) based on cluster associations with patient characteristics of interest, like genetic variants and comorbidities. As such, we intend to propose novel hypotheses regarding optimal prescribing practices in the context of chronic disease management. Additionally, our work will test the ability of these methods to generalize to real-world EHR data (as apposed to clinical trial or registry data).

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Bhargav Vemuri - Graduate Trainee, University of Washington

The Effects of Prescription Drug Price Shocks for Chronic Disease Patients

My research seeks to analyze the effects of changes in prescription drug prices on the usage of the drugs by chronic disease patients, their health behaviors, and their overall health outcomes. These exogenous price shocks can stem from government policy…

Scientific Questions Being Studied

My research seeks to analyze the effects of changes in prescription drug prices on the usage of the drugs by chronic disease patients, their health behaviors, and their overall health outcomes. These exogenous price shocks can stem from government policy changes, patent loss exclusivity, and innovation, among others. Usually, the price of the prescription drug will decrease, theoretically, allowing greater access to the drug. Focusing on patients with chronic diseases, who likely needed these drugs to live, raises the question of how they allocate their cost savings resulting from the price changes. Chronic disease patients may shift resources toward managing comorbidities or altering health behaviors like nutrition or utilization of remote patient monitors further improving overall health outcomes. Understanding these effects will help guide drug pricing policies, such as Medicare's future drug price negotiations.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

I will use the electronic medical records in the All of Us database to identify chronic disease patients and track their usage of prescription drugs and medical services before and after specific price shocks. I will perform causal inference using relevant econometric methods depending on the data’s structure. The method will likely be a difference-in-differences design comparing outcomes pre and post shock among chronic disease patients compared to a closely related group of patients unaffected by the shock due to age, type of insurance, disease, or another factor. This model will provide causal estimates of the effects of the drug price shocks on selected outcomes: number of prescriptions and dosage, related health behaviors, and certain health outcomes. If the data permits, I will analyze these effects with respect to race, income, and education to determine heterogeneous impacts.

Anticipated Findings

Based on economic theory, I anticipate the shocks will reduce drug prices and increase access. Individuals who realize cost savings will shift funds towards certain health improving behaviors like using remote patient monitors. I do not expect positive effects on all health behaviors because easier drug access may reduce the relative risk of some negative health behaviors i.e., moral hazard. I believe nutrition and exercise are the outcomes most likely to face negative effects. I predict a reduction in short-term emergency health outcomes like a heart attack caused by improved drug access and health behaviors overall. This research will contribute to the existing literature by analyzing a variety of price shocks caused by under or never studied polices and patent losses across patients of differing chronic diseases. Exploring multiple chronic diseases will allow thorough examination of spillover effects on other diseases particularly common comorbidities of chronic disease patients.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Michael Cobar - Graduate Trainee, Tulane University

v7 of LearningWorkspace

This work is in preparation for analysis of the Nutrition for Precision Health (NPH) in the Workbench. Team members have less than a year to develop the NPH models as the NPH data will not become fully available until the…

Scientific Questions Being Studied

This work is in preparation for analysis of the Nutrition for Precision Health (NPH) in the Workbench. Team members have less than a year to develop the NPH models as the NPH data will not become fully available until the fourth year of our five year grant. Therefore, we want to be as experienced as possible working with existing All of Us data prior to access of the NPH data when it is available.

Project Purpose(s)

Other Purpose (This workspace is used for familiarization and preparation for analysis of the Nutrition for Precision Health (NPH) data in the Workbench. The purpose is to ensure that team members are well versed in developing models using the complex data in All of Us before the NPH data is available.)

Scientific Approaches

We will be exploring the genetic data, FitBit data, and EHR data to join data sets and test code and develop models that predict outcomes (e.g. blood pressure). We will be using the Cohort Builder, Dataset Builder and SQL code to explore and combine All of Us data.

Anticipated Findings

The primary purpose of these explorations are not research driven, that is, we are not trying to predict blood pressure from genetic, physical activity or health related variables. Rather the primary purpose is to ensure that our team is well versed in developing models using the complex data in All of Us before the NPH data is available.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Jacob Baxter - Senior Researcher, West Point, United States Military Academy

Duplicate of Demo - Hypertension Prevalence

We are using the All of Us Researcher Workbench interface to answer the question, "Is hypertension prevalence in the All of Us Research Program similar to hypertension prevalence in the 2015–2016 National Health and Nutrition Examination Survey (NHANES) ?". Clinical…

Scientific Questions Being Studied

We are using the All of Us Researcher Workbench interface to answer the question, "Is hypertension prevalence in the All of Us Research Program similar to hypertension prevalence in the 2015–2016 National Health and Nutrition Examination Survey (NHANES) ?". Clinical approaches to understanding and treating hypertension may benefit from the integration of a precision medicine approach that integrates data on environments, social determinants of health, behaviors, and genomic factors that contribute to hypertension risk. Hypertension is a major public health concern and remains a leading risk factor for stroke and cardiovascular disease.

Project Purpose(s)

Other Purpose (This work is an AoU demo project. Demo projects are efforts by the AoU Research Program designed to meet the program goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. As an approved demo project, this work was reviewed and overseen by the AoU Research Program Science Committee and the AoU Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use. )

Scientific Approaches

In this cross-sectional, population-based study, we used All of Us baseline data from patient (age>18) provided information (PPI) surveys and electronic health record (EHR) blood pressure measurements and retrospectively examined the prevalence of hypertension in the EHR cohort using Systemized Nomenclature of Medicine (SNOMED codes and blood pressure medications recorded in the EHR. We used the EHR data (SNOMED codes on 2 distinct dates and at least one hypertension medication) as the primary definition, and then add subjects with elevated systolic or elevated diastolic blood pressure on measurements 2 and 3 from PPI. We extracted each participant’s detailed dates of SNOMED code for essential hypertension from the Researcher Workbench table ‘cb_search_all_events’. We calculated an age-standardized HTN prevalence according to the age distribution of the U.S. Census, using 3 groups (18-39, 40-59, ≥ 60).

Anticipated Findings

The prevalence of hypertension in the All of Us cohort is similar to that of published literature. All of Us age-adjusted HTN prevalence was 27.9% compared to 29.6% in National Health and Nutrition Examination Survey. The All of Us cohort is a growing source of diverse longitudinal data that can be utilized to study hypertension nationwide. The prevalence of hypertension varies in the United States (U.S.) by age, sex, and socioeconomic status. Hypertension can often be treated successfully with medication, and prevented or delayed with lifestyle modifications. Even with these established hypertension intervention and prevention strategies, the prevalence of hypertension continues to be at levels of public health concern. The diversity within All of Us may provide insight into factors relevant to hypertension prevention and treatments in a variety of social and geographic contexts and population strata in the U.S.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Huiding Chen - Project Personnel, Vanderbilt University

LearningWorkspace_v7

This work is in preparation for analysis of the Nutrition for Precision Health (NPH) in the Workbench. Team members have less than a year to develop the NPH models as the NPH data will not become fully available until the…

Scientific Questions Being Studied

This work is in preparation for analysis of the Nutrition for Precision Health (NPH) in the Workbench. Team members have less than a year to develop the NPH models as the NPH data will not become fully available until the fourth year of our five year grant. Therefore, we want to be as experienced as possible working with existing All of Us data prior to access of the NPH data when it is available.

Project Purpose(s)

Other Purpose (This workspace is used for familiarization and preparation for analysis of the Nutrition for Precision Health (NPH) data in the Workbench. The purpose is to ensure that team members are well versed in developing models using the complex data in All of Us before the NPH data is available.)

Scientific Approaches

We will be exploring the genetic data, FitBit data, and EHR data to join data sets and test code and develop models that predict outcomes (e.g. blood pressure). We will be using the Cohort Builder, Dataset Builder and SQL code to explore and combine All of Us data.

Anticipated Findings

The primary purpose of these explorations are not research driven, that is, we are not trying to predict blood pressure from genetic, physical activity or health related variables. Rather the primary purpose is to ensure that our team is well versed in developing models using the complex data in All of Us before the NPH data is available.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Jessica Starck - Project Personnel, West Point, United States Military Academy

Registered dietition

To find the relationship between distribution of Medical Nutrition Therapy usage and distribution of registered dietitian To find the relationship between the distrubution of dietitian and health outcome If these relationships exist, and the resources are inadequate, we need to…

Scientific Questions Being Studied

To find the relationship between distribution of Medical Nutrition Therapy usage and distribution of registered dietitian
To find the relationship between the distrubution of dietitian and health outcome

If these relationships exist, and the resources are inadequate, we need to do something to improve resource allocation, to improve health outcome, reduce health imparities.

Project Purpose(s)

Population Health

Scientific Approaches

To focus on the dataset including claims number of medical nutrition therapy(MNT); number of beneficiaries of MNT services; and claims about chronic diseases need MNT to control, like diabetes, hypertension, obesity, etc.

Anticipated Findings

There is an association between the distribution of registered dietitians and health outcomes, to find the reasons for the uneven distribution of registered dietitians, and improve resource allocation , finally to reduce health disparities in different places.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Siyu Wang - Graduate Trainee, Lehigh University

Vision Urbana- SI

We intend to determine to social, nutritional, and behavioral factors that cause someone to develop cardiovascular disease. Moreover, we want to create a questionnaire, that will highlight people that are at high risk of getting CVD.

Scientific Questions Being Studied

We intend to determine to social, nutritional, and behavioral factors that cause someone to develop cardiovascular disease. Moreover, we want to create a questionnaire, that will highlight people that are at high risk of getting CVD.

Project Purpose(s)

Disease Focused Research (Obesity/ cardiovascular disease)
Social / Behavioral

Scientific Approaches

We will focus on individuals from the Hispanic community, 50yrs and older, and living in the lower east side of Manhattan in the NYCHA housing projects.

Anticipated Findings

We expect to find out what are risky behaviors and traits that would cause someone to get cardiovascular disease.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Stephanie Izard - Project Personnel, Northwell Health

Collaborators:

Shayaa Muhammad - Project Personnel, City University of New York (CUNY)

Vision Urbana

We intend to determine to social, nutritional, and behavioral factors that cause someone to develop cardiovascular disease. Moreover, we want to create a questionnaire, that will highlight people that are at high risk of getting CVD.

Scientific Questions Being Studied

We intend to determine to social, nutritional, and behavioral factors that cause someone to develop cardiovascular disease. Moreover, we want to create a questionnaire, that will highlight people that are at high risk of getting CVD.

Project Purpose(s)

Disease Focused Research (obesity/cardiovascular disease)
Social / Behavioral

Scientific Approaches

We will focus on individuals from the Hispanic community, 50yrs and older, and living in the lower east side of Manhattan in the NYCHA housing projects.

Anticipated Findings

We expect to find out what are risky behaviors and traits that would cause someone to get cardiovascular disease.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Shayaa Muhammad - Project Personnel, City University of New York (CUNY)

Collaborators:

Stephanie Izard - Project Personnel, Northwell Health

Duplicate of Demo - Hypertension Prevalence

We are using the All of Us Researcher Workbench interface to answer the question, "Is hypertension prevalence in the All of Us Research Program similar to hypertension prevalence in the 2015–2016 National Health and Nutrition Examination Survey (NHANES) ?". Clinical…

Scientific Questions Being Studied

We are using the All of Us Researcher Workbench interface to answer the question, "Is hypertension prevalence in the All of Us Research Program similar to hypertension prevalence in the 2015–2016 National Health and Nutrition Examination Survey (NHANES) ?". Clinical approaches to understanding and treating hypertension may benefit from the integration of a precision medicine approach that integrates data on environments, social determinants of health, behaviors, and genomic factors that contribute to hypertension risk. Hypertension is a major public health concern and remains a leading risk factor for stroke and cardiovascular disease.

Project Purpose(s)

Other Purpose (This work is an AoU demo project. Demo projects are efforts by the AoU Research Program designed to meet the program goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. As an approved demo project, this work was reviewed and overseen by the AoU Research Program Science Committee and the AoU Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use. )

Scientific Approaches

In this cross-sectional, population-based study, we used All of Us baseline data from patient (age>18) provided information (PPI) surveys and electronic health record (EHR) blood pressure measurements and retrospectively examined the prevalence of hypertension in the EHR cohort using Systemized Nomenclature of Medicine (SNOMED codes and blood pressure medications recorded in the EHR. We used the EHR data (SNOMED codes on 2 distinct dates and at least one hypertension medication) as the primary definition, and then add subjects with elevated systolic or elevated diastolic blood pressure on measurements 2 and 3 from PPI. We extracted each participant’s detailed dates of SNOMED code for essential hypertension from the Researcher Workbench table ‘cb_search_all_events’. We calculated an age-standardized HTN prevalence according to the age distribution of the U.S. Census, using 3 groups (18-39, 40-59, ≥ 60).

Anticipated Findings

The prevalence of hypertension in the All of Us cohort is similar to that of published literature. All of Us age-adjusted HTN prevalence was 27.9% compared to 29.6% in National Health and Nutrition Examination Survey. The All of Us cohort is a growing source of diverse longitudinal data that can be utilized to study hypertension nationwide. The prevalence of hypertension varies in the United States (U.S.) by age, sex, and socioeconomic status. Hypertension can often be treated successfully with medication, and prevented or delayed with lifestyle modifications. Even with these established hypertension intervention and prevention strategies, the prevalence of hypertension continues to be at levels of public health concern. The diversity within All of Us may provide insight into factors relevant to hypertension prevention and treatments in a variety of social and geographic contexts and population strata in the U.S.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Tangirul Islam - Project Personnel, City University of New York (CUNY)

CCDGS_GeneralPediatricCohortReview

Our center studies genetic mechanisms that drives pediatric diseases in diverse populations. We have a couple of ongoing studies on hypertension, malnutrition, and alloimmunization progression that uses both clinical data as well as a handful candidate genetic variants to access…

Scientific Questions Being Studied

Our center studies genetic mechanisms that drives pediatric diseases in diverse populations. We have a couple of ongoing studies on hypertension, malnutrition, and alloimmunization progression that uses both clinical data as well as a handful candidate genetic variants to access the effects of genes. To access the diverse populations enriched with those candidate genetic variants, we need to formulate our search into SQL statements and calculate the number of samples and summarize the clinical characteristic of the samples. We also need to merge the EHR data with genetic variant data to create dataset for statistical analysis. This workspace will contain demos on 1) how to refine clinical criterion based on different EHR tables 2) how to merge demographic, clinical, and genetic variants data in an efficient way 3) Given a dataset, how to generate based descriptive statistics to evaluate the validity of the cohort.

Project Purpose(s)

Educational

Scientific Approaches

We are going to write out SQL statement that select observation data within the reasonable time frame of our clinical end point. We are going to tabulate the number of samples by self-report ethnicity, ancestry estimated from genomic data, age, and other important factors. We are going to summarized the clinical data related to the phenotypes, stratified by ancestry, age and gender. Some examples of the clinical data would be: blood pressure (hypertension project), BMI, weight, height, related diseases (malnutrition), and antibody reactions (alloimmunization). Our approach is to use SQL to select the appropriate EHR to be evaluated, to use SQL to get an estimation on sample size, and to use R code to create summary report on the cohorts. This will be a pipeline that other can follow if they would like to use AoU data to select observation cohort for any given diseases.

Anticipated Findings

We expect that demo will help others to understand how to use web tool query to start exploratory search on possible cohort for their study of interest. Our code will help others to filter through EHR longitudinal records and identify the relevant ones efficiently. We expect our SQL statement will perform well even for huge number of cohorts. This will prepare our lab members knowledge and code to embank their own large scale analysis. Other research may follow the similar strategy to create their cohort and build dataset. This will serve as a repository for code, pipelines for other researchers who would want to use the similar AoU data query approach.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Qing Li - Research Associate, National Human Genome Research Institute (NIH-NHGRI)

Collaborators:

Zelene Desire - Undergraduate Student, National Human Genome Research Institute (NIH-NHGRI)
Allyson Motter - Research Assistant, National Human Genome Research Institute (NIH-NHGRI)

Duplicate of Multivariate trajectories of treatment response in chronic disease

Treatment response in chronic diseases is a multifaceted and heterogeneous process that evolves over time and differs per patient. In practice, it is often defined as a binary outcome at a single point in time, which discards valuable temporal information.…

Scientific Questions Being Studied

Treatment response in chronic diseases is a multifaceted and heterogeneous process that evolves over time and differs per patient. In practice, it is often defined as a binary outcome at a single point in time, which discards valuable temporal information. The goal of this proposed research is to use multivariate temporal data to identify clusters of patients with different therapeutic response trajectories and potentially different optimal treatments for diabetic kidney disease and immune-mediated inflammatory diseases. This work can inform future therapeutic decision-making, trial design, and drug discovery for patients with chronic diseases. This work will also provide a foundation that can be extended to other modalities (metabolome, microbiome, nutrition, wearables) as real-world datasets increase in volume and complexity.

Project Purpose(s)

Disease Focused Research (immune-mediated inflammatory diseases)
Social / Behavioral
Drug Development
Methods Development
Ancestry

Scientific Approaches

By applying unsupervised clustering methods (VaDER, CRLI) to multivariate longitudinal measures (laboratory results, vital signs, physical measurements) from real-world datasets (All of Us, UK Biobank), we will identify common response trajectories to chronic disease therapies grouped by mechanisms of action. Leveraging diverse data (health records, participant surveys, genomics) we will perform quantitative and qualitative assessment of associations between identified trajectory clusters and baseline patient characteristics (demographics, comorbidities, medication use, social determinants of health, genetic variants).

Anticipated Findings

We anticipate the discovery of discrete IMID and DKD patient clusters based on their variable response to treatment across a number of longitudinal clinical measures. We will characterize our cluster results from two deep learning-based methods (VaDER, CRLI) based on cluster associations with patient characteristics of interest, like genetic variants and comorbidities. As such, we intend to propose novel hypotheses regarding optimal prescribing practices in the context of chronic disease management. Additionally, our work will test the ability of these methods to generalize to real-world EHR data (as apposed to clinical trial or registry data).

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Bhargav Vemuri - Graduate Trainee, University of Washington

Lifestyle Associations in Inflammatory Skin Diseases

Hidradenitis suppurativa (HS) is a chronic inflammatory skin condition characterized by painful nodules, draining sinus tunnels, and subsequent fibrotic scarring primarily in intertriginous areas. The pathogenesis of HS remains poorly understood but several environmental factors have been associated with HS…

Scientific Questions Being Studied

Hidradenitis suppurativa (HS) is a chronic inflammatory skin condition characterized by painful nodules, draining sinus tunnels, and subsequent fibrotic scarring primarily in intertriginous areas. The pathogenesis of HS remains poorly understood but several environmental factors have been associated with HS including obesity, occluded skin, friction, and smoking.

The exact associations of cigarette smoking, e-cigarette exposure, cigar smoking, hookah smoking in hidradenitis suppurativa and other related inflammatory skin diseases is still unclear. In addition, lifestyle associations such as exercise, fitness data, and nutrition information is also under explored. Since hidradenitis suppurativa also has a hormonal component it will also be interesting to explore sex/gender associations and other hormonal aspects of the disease that are available and compare to other inflammatory skin diseases.

Project Purpose(s)

Disease Focused Research (hidradenitis suppurativa)

Scientific Approaches

We will use the All of US data sets to explore the surveys on demographic information and lifestyle information for patients with hidradenitis suppurativa and other inflammatory skin diseases such as psoriasis, acne, and lichen planus.

Anticipated Findings

We anticipate to find certain associations with smoking behavior and hidradenitis suppurativa that may be similar or that may differ from other inflammatory skin diseases. This will inform clinicians and researchers on treatment options, counseling on smoking cessation, and future pathophysiology research areas to explore.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

William Shipman - Research Fellow, Yale University

Allergies Research

Our initial question stemmed from personal anecdotes, prompting us to inquire about the impact of smaller, seemingly inconsequential factors like food, stress, and other variables on our immune system and their potential to exacerbate seasonal allergies. Knowing that most seasonal…

Scientific Questions Being Studied

Our initial question stemmed from personal anecdotes, prompting us to inquire about the impact of smaller, seemingly inconsequential factors like food, stress, and other variables on our immune system and their potential to exacerbate seasonal allergies. Knowing that most seasonal allergy symptoms, such as sneezing, coughing and itching, can get worse when there is a high pollen count. We want to learn more about seasonal allergies in general and learn how these allergies affect us in ways more than just the amount of pollen in the air. We found out through personal experience that eating particular foods will either make our allergic reactions worse or better. After a brief research, we discovered that nutrition does in fact have an impact on seasonal allergies and that stress can aggravate allergies by increasing histamine levels in the bloodstream. Additionally, we are intrigued to know how a specialized diet can possibly reduce allergic reactions amid extreme environmental changes.

Project Purpose(s)

Disease Focused Research (Seasonal allergies )
Population Health
Methods Development
Ancestry

Scientific Approaches

What effect can diet have on seasonal allergies?
We will compare different statistics to figure out the n of people that find certain foods affecting their seasonal allergies, as well as gather more research and find out if food can actually affect seasonal allergies, mainly focusing on the people that do not have any food allergies.

Anticipated Findings

Our findings will contribute to the scientific knowledge in the field because it could help others who are struggling with seasonal allergies. We could possibly find out what foods to avoid during the times that there are environmental changes to reduce the side effects allergies can bring.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Betzabell Velasco - Undergraduate Student, Arizona State University

CCDGS_WebToolsQueryDemo

Our center studies genetic mechanisms that drives pediatric diseases in diverse populations. We have a couple of ongoing studies on hypertension, malnutrition, and alloimmunization progression that uses both clinical data as well as a handful candidate genetic variants to access…

Scientific Questions Being Studied

Our center studies genetic mechanisms that drives pediatric diseases in diverse populations. We have a couple of ongoing studies on hypertension, malnutrition, and alloimmunization progression that uses both clinical data as well as a handful candidate genetic variants to access the effects of genes. To access the diverse populations enriched with those candidate genetic variants, we need to formulate our search into SQL statements and calculate the number of samples and summarize the clinical characteristic of the samples. We also need to merge the EHR data with genetic variant data to create dataset for statistical analysis. This workspace will contain demos on 1) how to refine clinical criterion based on different EHR tables 2) how to merge demographic, clinical, and genetic variants data in an efficient way 3) Given a dataset, how to generate based descriptive statistics to evaluate the validity of the cohort.

Project Purpose(s)

Educational

Scientific Approaches

We are going to write out SQL statement that select observation data within the reasonable time frame of our clinical end point. We are going to tabulate the number of samples by self-report ethnicity, ancestry estimated from genomic data, age, and other important factors. We are going to summarized the clinical data related to the phenotypes, stratified by ancestry, age and gender. Some examples of the clinical data would be: blood pressure (hypertension project), BMI, weight, height, related diseases (malnutrition), and antibody reactions (alloimmunization). Our approach is to use SQL to select the appropriate EHR to be evaluated, to use SQL to get an estimation on sample size, and to use R code to create summary report on the cohorts. This will be a pipeline that other can follow if they would like to use AoU data to select observation cohort for any given diseases.

Anticipated Findings

We expect that demo will help others to understand how to use web tool query to start exploratory search on possible cohort for their study of interest. Our code will help others to filter through EHR longitudinal records and identify the relevant ones efficiently. We expect our SQL statement will perform well even for huge number of cohorts. This will prepare our lab members knowledge and code to embank their own large scale analysis. Other research may follow the similar strategy to create their cohort and build dataset. This will serve as a repository for code, pipelines for other researchers who would want to use the similar AoU data query approach.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Qing Li - Research Associate, National Human Genome Research Institute (NIH-NHGRI)

Collaborators:

Zelene Desire - Undergraduate Student, National Human Genome Research Institute (NIH-NHGRI)
Thalia Billawala - Research Assistant, National Human Genome Research Institute (NIH-NHGRI)
Allyson Motter - Research Assistant, National Human Genome Research Institute (NIH-NHGRI)

Impacts of Physical Activity and Nutrition on Health

Some of the specific questions we intend to study are: Are there significant differences between Blacks and Latinos in regard to physical activity and nutrition, Are there any barriers that prevent access to proper nutrition and physical activity, and What…

Scientific Questions Being Studied

Some of the specific questions we intend to study are: Are there significant differences between Blacks and Latinos in regard to physical activity and nutrition, Are there any barriers that prevent access to proper nutrition and physical activity, and What are the impacts on adverse health for depression and anxiety? These three questions are important for understanding if race is a factor in health disparities, and how we can promote better nutrition and more physical activity across communities in East Los Angeles. After doing a literature review on several different articles related to nutrition and physical activity, some of the gaps in the findings included factors that affect nutritional intake and physical activity the most, and the impacts that they can have on mental health. As it’s not well understood how physical health can impact mental health, the three questions are relevant for decreasing health disparities and increasing awareness for better physical and mental health.

Project Purpose(s)

Educational

Scientific Approaches

Appropriate statistical analyses will be used to determine if significant differences or relationships exist between the parameters being measured. The All of Us dataset will be used by creating creating concept sets from physical measurements, fitbit step data, and survey data (Basics, Overall Health, Personal Health and SDoH) to look at the impact the social and built environment and socioeconomic status has on physical activity and health.

Anticipated Findings

We anticipate finding a positive relationship between high income Black and Latino households and proper access to nutrition and physical activity. We also anticipate finding behavior and low income to be some of the major causes for barriers that prevent proper nutrition and physical activity for Blacks and Latinos. Finally, we anticipate finding an increased risk of depression and anxiety as a result of poor nutrition and physical activity. By identifying any barriers that hinder access to proper nutrition and physical activity, our findings would contribute to the body of scientific knowledge by helping in creating behavioral interventions and policies for improved nutrition and physical activity in Black and Latino households, specifically in East Los Angeles.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Alexis Nebrensky - Undergraduate Student, California State University Los Angeles

Impacts of Physical Activity and Nutrition on Health

Some of the specific questions we intend to study are: Are there significant differences between Blacks and Latinos in regard to physical activity and nutrition, Are there any barriers that prevent access to proper nutrition and physical activity, and What…

Scientific Questions Being Studied

Some of the specific questions we intend to study are: Are there significant differences between Blacks and Latinos in regard to physical activity and nutrition, Are there any barriers that prevent access to proper nutrition and physical activity, and What are the impacts on adverse health for depression and anxiety? These three questions are important for understanding if race is a factor in health disparities, and how we can promote better nutrition and more physical activity across communities in East Los Angeles. After doing a literature review on several different articles related to nutrition and physical activity, some of the gaps in the findings included factors that affect nutritional intake and physical activity the most, and the impacts that they can have on mental health. As it’s not well understood how physical health can impact mental health, the three questions are relevant for decreasing health disparities and increasing awareness for better physical and mental health.

Project Purpose(s)

Educational

Scientific Approaches

Appropriate statistical analyses will be used to determine if significant differences or relationships exist between the parameters being measured. The All of Us dataset will be used by creating creating concept sets from physical measurements, fitbit step data, and survey data (Basics, Overall Health, Personal Health and SDoH) to look at the impact the social and built environment and socioeconomic status has on physical activity and health.

Anticipated Findings

We anticipate finding a positive relationship between high income Black and Latino households and proper access to nutrition and physical activity. We also anticipate finding behavior and low income to be some of the major causes for barriers that prevent proper nutrition and physical activity for Blacks and Latinos. Finally, we anticipate finding an increased risk of depression and anxiety as a result of poor nutrition and physical activity. By identifying any barriers that hinder access to proper nutrition and physical activity, our findings would contribute to the body of scientific knowledge by helping in creating behavioral interventions and policies for improved nutrition and physical activity in Black and Latino households, specifically in East Los Angeles.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Jacob Ramos - Undergraduate Student, California State University Los Angeles

Malnutrition_Workspace_Jared_Redmond

Some scientific questions that we intend to study revolve around the role genetics plays in people developing a severe malnutrition disease. It is important that we research the data applicable to our question because we can get a better sense…

Scientific Questions Being Studied

Some scientific questions that we intend to study revolve around the role genetics plays in people developing a severe malnutrition disease. It is important that we research the data applicable to our question because we can get a better sense for the onset of the disease and help formulate medical interventions.

Project Purpose(s)

Disease Focused Research (protein-energy malnutrition)
Population Health
Methods Development
Ancestry

Scientific Approaches

We plan to look at individuals who currently have or had severe malnutrition diseases. These individuals will be filtered further by different criteria that involves their demographics, specific conditions, and physical measurements. This cohort will be compared to another cohort with people who have are variants of interest to see if there is any overlap.

Anticipated Findings

We anticipate to find a pattern of overlap between people in our first cohort and people in our second cohort. This will give us a better sense of the genetic variants that are likely involved in people developing a severe malnutrition disease.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Jared Redmond - Research Fellow, National Human Genome Research Institute (NIH-NHGRI)

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