Research Projects Directory

Research Projects Directory

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

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

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

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

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

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Health & Wealth

Project Purpose(s)

  • Disease Focused Research (Cardiovascular disease and respiratory illnesses)
  • Population Health ...
  • Ancestry

Scientific Questions Being Studied

Is there a correlation between Income, CVD, and respiratory illnesses? Why are low income populations/ communities at a higher risk of developing preventable diseases? What can be done to decrease the risk of developing CVD and respiratory illness among disadvantaged communities?
These questions are extremely important because the question itself as well as the solution establishes a greater understanding of health disparities and how to reduce them. Underprivileged populations are just as important to research and to learn more about.

Scientific Approaches

I will be using R software, the Jupyter notebook offered through the All of Us workstations, and Google Earth to determine causes for increased risk in underdeveloped communities.

Anticipated Findings

I anticipate that there will be a correlation between all of the variables mentioned in the research questions. Based on results from genetic findings I hope to bring awareness to the main causes for the increased risk of preventable diseases. I anticipate that Google Earth and geographic data will allow for a deeper understanding of the health disparities in certain regions and why these regions are more or less susceptible to increased preventable disease risk..

Demographic Categories of Interest

  • Race / Ethnicity
  • Income Level

Research Team

Owner:

  • Jo-el Banini - Undergraduate Student, University of Arizona

Health & Wealth

Project Purpose(s)

  • Disease Focused Research (Cardiovascular disease and respiratory illnesses)
  • Population Health ...
  • Ancestry

Scientific Questions Being Studied

Is there a correlation between levels of household income, and CVD, and respiratory illnesses? Are low income populations/ communities at a higher risk of developing preventable diseases? What can be done to decrease the risk of developing CVD and respiratory illness among these communities?
These questions as well as the solution establishes a greater understanding of health disparities and how to reduce them.

Scientific Approaches

I will be using R software and the Jupyter notebook offered through the All of Us workstation, to examine the correlation between income and CVD and how the association of risk factors with CVD differs by income level. I will be using logistic regression to test the main effects and effect modifications in income and other risk factors.

Anticipated Findings

I anticipate that there will be a correlation between all of the variables mentioned in the research questions. Based on these results, I hope to bring awareness to some of the possible causes for increased risk of preventable diseases in these communities. I anticipate that this research will allow people to gain a deeper understanding of the health disparities that affect certain populations and why these communities are more or less susceptible to increased preventable disease risk.

Demographic Categories of Interest

  • Income Level

Research Team

Owner:

  • Jo-el Banini - Undergraduate Student, University of Arizona

Collaborators:

  • Yann Klimentidis - Mid-career Tenured Researcher, University of Arizona

Gender Identity Algorithm

Project Purpose(s)

  • Population Health
  • Methods Development ...

Scientific Questions Being Studied

Understanding of the unique health needs of gender minorities, including transgender and gender non-binary individuals, is critical. Gender minorities face greater health disparities due in part to a lack of research into population-specific health concerns. Consequently, it is critical to understand health outcomes such as cancer risk s in this population. However, the primary challenge of studying cancer and other rare diseases among gender minorities is that they represent a hard-to-reach population and the collection of gender identity is often lacking in national surveys or healthcare databases. Previous investigations have used diagnosis codes to create algorithms to identify non-binary gender identity in large databases but lacked patient-reported gender identity. We seek to build an algorithm in All of Us, which uses self-reported gender identity (the gold standard), to identify and characterize the health of gender minorities more accurately.

Scientific Approaches

We propose to create an algorithm with diagnostic, procedure, and medication codes from electronic health records to identify transgender individuals using a gold standard for gender identity. We will use machine learning techniques to classify patients as transgender/non-binary or cisgender among the >100,000 individuals, including 400 transgender and 520 non-binary participants, in All of Us. Predictors of transgender status will be selected based on consultations with clinicians with expertise in transgender healthcare. These variables include sociodemographic (age, race/ethnicity), ICD-9/10 diagnosis codes (gender dysphoria), procedure codes for gender-affirming procedures (e.g. hysterectomy), and prescriptions for gender-affirming hormone therapy. We will use 10-fold cross-validation for the internal validation of the models. We will calculate sensitivity, specificity, positive predictive value, and negative predictive value to assess model performance.

Anticipated Findings

Previous investigations into the use of diagnosis codes for identifying non-binary gender identities did not have a gold standard for defining gender identity. We aim to create an algorithm that accurately defines transgender gender identity in large administrative databases to aid in future research efforts. The next step will be to apply this algorithm to other electronic health data such as Medicaid, Medicare, and private insurance databases. The ability to identify a population of transgender patients in large healthcare datasets will be a boon to health research on transgender and non-binary individuals.

Demographic Categories of Interest

  • Sex at Birth
  • Gender Identity

Research Team

Owner:

  • Sarah Jackson - Research Fellow, NIH

Genetic architecture of complex traits

Project Purpose(s)

  • Population Health
  • Social / Behavioral ...
  • Methods Development
  • Ancestry

Scientific Questions Being Studied

Genome-wide association studies (GWAS) have identified tens of thousands of associations for numerous diseases and traits, but interpretation of GWAS findings remains challenging. The complex structure of linkage disequilibrium in the human genome and weak effect sizes of common genetic variants encumber us to identify biologically functional genetic variants. Leveraging rich phenotypic information in AllofUs and various types of functional annotations of the human genome, this project aims to use sophisticated data integration techniques to provide insights into the genetic basis of various human diseases and traits. More specifically, we will integrate GWAS data from external resources, GWAS data for a variety of complex traits in AllofUs, and epigenetic and transcriptomic functional annotation data in public repositories, to study the genetic basis of complex human phenotypes.

Scientific Approaches

Since a major goal of this study is to systematically investigate the genetic basis of various human phenotypes, we would like to request the full AllofUs cohort subjects. We will conduct genome-wide association studies (GWAS) to identify genetic variants associated with diseases and traits. In addition, we will apply a suite of post-GWAS analytic approaches developed in our group to interpret genetic associations and build predictive models for various phenotypes. We will analyze data of different race/ethnic groups separately to reduce potential confounding effects of population stratification. We will combine association results from different populations through meta-analysis.

Anticipated Findings

Our results will deepen our understanding of the molecular and genetic basis of various complex diseases and traits. Additionally, although functional genome annotations have become increasing accessible, their utility in complex trait genetics still remains under-explored. Our proposed project will reveal novel genotype-phenotype associations and demonstrate how leveraging functional annotation information in genetic analysis can provide biological insights into complex phenotypes. Finally, the methods we develop for this project will be publicly available to the research community.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Qiongshi Lu - Early Career Tenure-track Researcher, University of Wisconsin, Madison

Genetic data

Project Purpose(s)

  • Educational ...

Scientific Questions Being Studied

We are studying how genetic data may be incorporated into electronic health records, exploring the data formats and interoperability issues as well as data presentation needs for clinicians and patients.

Scientific Approaches

We will use various methods of data visualization and thinkaloud usability protocols to test ability to visualize the data in meaningful ways with value for the clinician decision maker, and possibly use the FHIR standard for data interoperability.

Anticipated Findings

The findings will help us understand how to incorporate genetic data with other clinical data for decision making, and what are the most efficient and usable ways to visualize data for this purpose.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Yalini Senathirajah - Early Career Tenure-track Researcher, University of Pittsburgh

Genetics of Behavior

Project Purpose(s)

  • Disease Focused Research (psychiatry)
  • Social / Behavioral ...
  • Methods Development
  • Control Set
  • Ancestry

Scientific Questions Being Studied

This is just a placeholder.
I am interested in studying genetics of human behavior, both normal and abnormal. But I will need to study the data first, before I can formulate my scientific questions. Will come back to revise this after studying the data.

Scientific Approaches

Not sure yet. I need to study the data first.
Data list:
Statistical tools:

Anticipated Findings

Hope to learn:
What are the genes affecting human behavior; affecting the risk of developing disorders;
Also, how different genetic and environmental factors interact to module human behavior and disease risk.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • CHUNYU LIU - Late Career Tenured Researcher, SUNY Upstate Medical University

Genomic risk prediction of substance dependence in diverse populations

Project Purpose(s)

  • Disease Focused Research (substance dependence)
  • Population Health ...
  • Ancestry

Scientific Questions Being Studied

Genome-wide association studies of substance addiction and dependence have identified variants that contribute to a small portion of the total disorder variance. While these data have been useful in identifying novel genetic associations, in large part these data have not been used predict risk of disease. Recently, groups have begun leverage the contribution of variants that do not reach the level of genome-wide significance to improve the prediction of complex diseases. Unfortunately, these measures have not been developed or applied to non-European populations. Our hope is leverage the All of Us data to estimate disease effect estimates for substance use in diverse populations so as to be able to appropriately apply a ancestry-tailored risk component to polygenic risk scores involving admixed populations (i.e., populations admixed with multiple continental including European ancestry).

Scientific Approaches

We will cover both non-Hispanic white population and black populations in this study. Three types of phenotypes for substance use will be investigated: 1) alcohol use; 2) smoking; 3) Opioid use.
Aim 1: perform GWAS, create and validate PRS in All-of-US dataset
A standard GWAS will be conducted. PRS will be created by a few different variant sets and approaches. Approaches include BSLMM, LDPred, PRSice and Lasso penalized regression. These PRSs will be evaluated in the second half of All-of-Us cohort. The one with highest Area under ROC curve, or other discrimination metrics will be selected.
Aim 2: test and evaluate PRS in internal testing dataset from a health system
We will test PRS in our study populations consisting of European Americans (EA) and African Americans (AA) individuals. Testing risk scores in African Americans will involve applying the appropriate risk estimate for African and European ancestry to each individual based on their admixture and local ancestry.

Anticipated Findings

1. Ancestry-shared and ancestry-specific susceptibility loci for alcohol, smoking and opioid use and dependence.
2. Optimized PRS for each disease or phenotype in European population and African-descent populations.
3. Clinically actionable risk prediction model incorporating PRS, family history, and environmental factors will be built to help patient treatment and risk management.
4. This will greatly improve risk prediction in non-white, minority populations for substance use.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age

Research Team

Owner:

  • Hongsheng Gui - Early Career Tenure-track Researcher, Henry Ford Health System

Genomic risk prediction of substance dependence in diverse populations

Project Purpose(s)

  • Disease Focused Research (substance dependence)
  • Population Health ...
  • Ancestry

Scientific Questions Being Studied

Genome-wide association studies of substance addiction and dependence have identified variants that contribute to a small portion of the total disorder variance. While these data have been useful in identifying novel genetic associations, in large part these data have not been used predict risk of disease. Recently, groups have begun leverage the contribution of variants that do not reach the level of genome-wide significance to improve the prediction of complex diseases. Unfortunately, these measures have not been developed or applied to non-European populations. Our hope is leverage the All of Us data to estimate disease effect estimates for substance use in diverse populations so as to be able to appropriately apply a ancestry-tailored risk component to polygenic risk scores involving admixed populations (i.e., populations admixed with multiple continental including European ancestry).

Scientific Approaches

We will cover both non-Hispanic white population and black populations in this study. Three types of phenotypes for substance use will be investigated: 1) alcohol use; 2) smoking; 3) Opioid use.
Aim 1: perform GWAS, create and validate PRS in All-of-US dataset
A standard GWAS will be conducted. PRS will be created by a few different variant sets and approaches. Approaches include BSLMM, LDPred, PRSice and Lasso penalized regression. These PRSs will be evaluated in the second half of All-of-Us cohort. The one with highest Area under ROC curve, or other discrimination metrics will be selected.
Aim 2: test and evaluate PRS in internal testing dataset from a health system
We will test PRS in our study populations consisting of European Americans (EA) and African Americans (AA) individuals. Testing risk scores in African Americans will involve applying the appropriate risk estimate for African and European ancestry to each individual based on their admixture and local ancestry.

Anticipated Findings

1. Ancestry-shared and ancestry-specific susceptibility loci for alcohol, smoking and opioid use and dependence.
2. Optimized PRS for each disease or phenotype in European population and African-descent populations.
3. Clinically actionable risk prediction model incorporating PRS, family history, and environmental factors will be built to help patient treatment and risk management.
4. This will greatly improve risk prediction in non-white, minority populations for substance use.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age

Research Team

Owner:

  • Hongsheng Gui - Early Career Tenure-track Researcher, Henry Ford Health System

Genomics of psychiatric-related traits in the Latinx population

Project Purpose(s)

  • Disease Focused Research (Psychiatric disorders)
  • Population Health ...
  • Methods Development
  • Ancestry

Scientific Questions Being Studied

This study seeks to conduct a large-scale genome-wide association study (GWAS) of psychiatric disorders in Latinx populations. Latinx cohorts have been greatly understudied in psychiatric genomic studies. Further, they have complicated population histories, with multiple waves of admixture between several ancestral groups.Our aim is to identify genetic variants associated with psychiatric-related traits in Latinx populations and generate polygenic risk scores that are specific to this population. We expect to conduct the largest GWAS of different psychiatric traits in the Latinx population. This number could increase even further based on our current efforts in identifying additional Latinx cohorts by leading the newly-created Latin American Genomics Consortium, which includes the participation of over 50 investigators from 7 different Latin American countries and the US.

Scientific Approaches

We will apply a newly developed GWAS method, Tractor, to account for admixture in the All of Us Latinx cohort. The Tractor framework, in development by collaborators, shows promise in correcting for fine-scale population structure at the level of haplotypes, improving long-range phase in admixed cohorts, and boosting GWAS power in case-control settings of similar sample, prevalence, and effect sizes expected for our cohort aggregations and phenotypes. We will test and optimize Tractor here for best performance in multi-way admixed Latinx cohorts in preparation to run local ancestry aware association studies in the All of Us cohort. The All of Us cohort offers a unique opportunity to test the method in a large Latinx population (n=47,580). We will also meta-analyze the All of Us cohort with additional Latinx cohorts; we expect to include ~100K individuals.

Anticipated Findings

We will identify novel genetic variants associated with psychiatric-related traits in Latinx populations. Our findings will increase our understanding of the genetic risk factors of psychiatric disorders specific to Latinx, a greatly understudied population, thus decreasing health disparities in the psychiatric field.

Demographic Categories of Interest

  • Race / Ethnicity

Research Team

Owner:

  • Janitza Montalvo-Ortiz - Early Career Tenure-track Researcher, Yale University

gwas-prs

Project Purpose(s)

  • Ancestry ...

Scientific Questions Being Studied

Large-scale genomic studies can be used to inform individual-level risk of a specific condition compared to population based on their genetic constitution. I plan to combine genome-wide association study (GWAS) SNPs to calculate polygenic risk score (PRS) for various phenotypes. Based on PRS, I aim to identify genetic factors that contribute to various cognitive function and socioeconomic status.

Scientific Approaches

Calculation of PRS requires statistical modeling of population while accounting for various biases and population effects. After carefully calculating PRS, various dimensionality reduction and data visualization techniques can be employed to understand the complex multi-dimentional structure of PRS. Using a linear model and more sophisticated machine learning algorithm, I plan to study and interpret genetic factors that contribute to quantifiable cognitive function.

Anticipated Findings

Findings can contribute to understanding the genetic basis of various neuropsychiatric disorders that impacts socioeconomic status.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Donghoon Lee - Early Career Tenure-track Researcher, Icahn School of Medicine at Mount Sinai

Health and Covid19

Project Purpose(s)

  • Population Health
  • Social / Behavioral ...
  • Educational

Scientific Questions Being Studied

Since the start of the covid19 pandemic, state and local governments have adopted a range of policies that are designed to reduce the transmission of the virus. Evidence suggests that such policies and private responses related to the pandemic affected the utilization of non-covid19 care.
Our Research will attempt to investigate:
1. Changes in health visits both overall and by type of diagnosis/reason for visit. We develop various measures to track in-person vs telehealth visits based on visit location, CPT code modifiers and services eligible and ineligible for telehealth billing.
2. Changes in outpatient procedures (e.g.: chemotherapy via infusion therapy, cancer screenings and cardiac stress testing) and laboratory orders (e.g.: A1C, LDL and HDL labs).
3. Changes in health biomarkers such as A1C values; Liver and Lipid blood work and hemoglobin levels. These biomarkers are often used in clinical research as measures of both current health status or future risk of disease.

Scientific Approaches

Difference in Differences
Event Study
and Interrupted Time series

Our empirical approach will mainly utilize variation in the timing and type of covid19 policy across states to estimate the effect of state closures and re-openings on healthcare and health.

Anticipated Findings

Estimates from our analysis will contribute to the research on the marginal benefit of additional medical care (Fisher et al. (2003)a,b; Skinner and Wennberg 1998). This work seeks to disentangle patient selection into care from the benefit of the care itself, usually with the goal of determining whether certain types of care reflect wasteful “flat of the curve” spending. Previous work on this topic finds mixed evidence. Our work will also inform policy, as we enter a second peak of covid19 infections, it is useful to understand what care was deferred and the consequences of this delay.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Engy Ziedan - Early Career Tenure-track Researcher, Tulane University

Health Disparities

Project Purpose(s)

  • Population Health ...

Scientific Questions Being Studied

Are there any disparities in pregnancies in medicaid expansion vs. non-expansion space.

Scientific Approaches

Not available.

Anticipated Findings

Medicaid expansion states will have better outcomes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Shaquille Peters - Project Personnel, Scripps Research

Healthy Aging

Project Purpose(s)

  • Disease Focused Research (Healthy aging and longevity)
  • Ancestry ...

Scientific Questions Being Studied

The overall goal of our research is to incorporate polygenic variants with longitudinally measured routine blood chemistries and phenotypic measures into prediction models for healthy aging and longevity. We have used the UK Biobank data to create polygenic longevity scores(PLS) and will explore the All of Us database to identify phenotypic and standard biomarkers that are predictors of health/longevity. When genetic information is available, we will develop PLS for more diverse populations. We are identifying a cohort of individuals with no diagnosed illnesses or measurements of ill health as the population of interest, and will then compare the presence of biomarkers in the healthy cohort to those in the ‘control’ group of individuals who have a diagnosed disease or measure of ill health. We are interested in understanding what repeated (longitudinal) measurements can help predict, since one time measurements do not give insight into the trajectory of an individual’s health.

Scientific Approaches

Extensive lists of healthy aging and longevity biomarkers, as well as clinical or phenotypic measurements, will be curated from the literature. For biomarkers that are now available from the All of Us data as repeated measures, we will use longitudinal data analysis methods such as mixed models. We will also consider additional multi-variate methods, such as multiple multivariate regression analysis and cluster analysis to assess phenomena such as dysregulation and potential heterogeneity across the participants. We will account for important phenotypic covariates and control for false positive findings using state-of the field false discovery rate analyses. One goal is to develop the actual risk prediction models using software such as iCARE https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7001949/

Anticipated Findings

We are hoping to identify new ways of incorporating standard clinical chemistries and phenotypic data with PLS from a racially and ethnically diverse population into a clinically useful model for assessing an individual’s overall good health The annual or general physical examination as it exists today, done for the average healthy adult has never been scientifically proven to actually improve health outcomes, specifically healthy aging or longevity. This may in part be due to a lack of an individualized approach to disease prevention. Building better longevity prediction tools for clinicians, especially ones that are developed in ethnically or racially diverse populations, is a necessary step in personalized or precision medicine. Ultimately such tools will help risk stratify the population and allow for more appropriate allotment of time and resources into keeping individuals healthy and preventing disease.

Demographic Categories of Interest

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

Research Team

Owner:

  • Laura Goetz - Early Career Tenure-track Researcher, Translational Genomics Research Institute

Hispanic cancer

Project Purpose(s)

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

Scientific Questions Being Studied

Despite lower incidence rates of more common cancers, such as breast, colon, lung, and prostate, Hispanics have a disproportionately higher incidence and mortality for cancers associated with infectious agents such as liver, cervical, and gastric cancer (GC). For example, GC incidence is 1.6 times higher in Hispanics compared to non-Hispanic Whites (NHW) with a nearly two-fold increase in mortality. The increase in mortality in Hispanics is often attributed to poor socioeconomic status and the resultant delayed presentation to health care systems.
To study the variation in the incidence of all cancers in Hispanics based on immigration status.

Scientific Approaches

Specific Aim 1: To assess the effect of acculturation on the incidence of Hispanic cancer
Research Plan:
.All of Us research program will be queried to identify participants with gastric cancer. The incidence of gastric cancer among immigrant and non-immigrant Hispanics will be assessed and compared with the non-Hispanic control group. Age, sex, country of birth, immigration status, socioeconomic status, lifestyle factors (smoking, alcohol), and comorbidities will be used in a multivariate logistic regression analysis to identify factors that are associated with gastric cancer in Hispanics. The impact of acculturation on variations in projected incidence will be calculated as described previously.

Specific Aim 2: To assess the incidence of cancer in Hispanic/ Latino ethnic subgroups

Anticipated Findings

We believe the incidence of cancers, particularly for gastric, cervical and liver cancers will be different based on the country of origin and immigration status.
Although, there is significant evidence about increased incidence and mortality of gastric cancer in Hispanics, there is very little to no evidence on the impact of acculturation on cancer incidence. The proposed study will help identify the impact of acculturation on cancer incidence. Additionally, the variations in the incidence of cancer among different Hispanic race/ethnic subgroups residing in United states combined with genomic data will give us an idea about the high-risk groups and the genomic variations that account for variations in incidence. Such comprehensive analysis combining population data with genomic data is currently not available for cancer in Hispanics. Data form this analysis will add new information that could be utilized to improve cancer outcomes in this race/ethnic subgroup.

Demographic Categories of Interest

  • Race / Ethnicity

Research Team

Owner:

  • Maheswari Senthil - Mid-career Tenured Researcher, University of California, Irvine

Hispanic gastric cancer

Project Purpose(s)

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

Scientific Questions Being Studied

Gastric cancer is one of the most aggressive gastrointestinal malignancies with a precipitous decrease in survival with increasing stages. Disturbing trends with increased incidence of gastric cancer in younger men and presentation at later stages are observed in Hispanics. Understanding the pathogenic drivers (environmental, lifestyle, and biologic) for the aggressive and later stage presentations is crucial to make an impact in the outcomes of gastric cancer in Hispanics. The proposed study will help identify the impact of acculturation and ethnic origin on gastric cancer incidence. Furthermore, genomic alterations that predispose to gastric cancer will provided deeper understanding about the biology of gastric cancer in Hispanics and may help identify treatment targets.

Scientific Approaches

All Hispanic participants from All of us research program will be queried to identify participants with gastric cancer. Age, sex, country of birth, immigration status, socioeconomic status, lifestyle factors ( smoking, alcohol), and comorbidities will be used in a multivariate logistic regression analysis to identify factors predictive of gastric cancer incidence in Hispanics.

Anticipated Findings

We anticipate that this analysis will give us information about the effects of acculturation on the incidence of gastric cancer. Additionally, the variations in the incidence of gastric cancer among different Hispanic race/ethnic subgroups residing in United states will give us an idea about the high-risk groups among Hispanics. Identification of high-risk factors will help develop criteria for screening to detect premalignant conditions and cancer at earlier stages. Due to the overall low incidence of gastric cancer in the United States, screening programs for gastric cancer will only be useful in specific high-risk groups. Knowledge about the high-risk group among Hispanics is currently lacking. Results from this study will add to the body of literature and help inform screening strategies to improve gastric cancer outcomes in Hispanics.

Demographic Categories of Interest

  • Race / Ethnicity

Research Team

Owner:

  • Maheswari Senthil - Mid-career Tenured Researcher, University of California, Irvine

hiv all of us

Project Purpose(s)

  • Educational ...

Scientific Questions Being Studied

An initial testing workspace to get oriented and develop further thoughts on usage of the data.

I'm particularly interested in what lab data is available including T cell subsets

Scientific Approaches

An initial testing workspace to get oriented and develop further thoughts on usage of the data.

Anticipated Findings

I expect to become more familiar with the all of us cohort and determine if I can successfully mine data for a grant application about immune recovery, Currently it is unclear why some people living with HIV on therapy have better immune recovery than others

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Rob striker - Mid-career Tenured Researcher, University of Wisconsin, Madison

HK_workspace_train_v1

Project Purpose(s)

  • Educational ...

Scientific Questions Being Studied

How breast cancer is diagnosed in different ancestral populations.

Scientific Approaches

Not available.

Anticipated Findings

Diagnoses are detected at different stages in different ancestral populations.

Demographic Categories of Interest

Not available.

Research Team

Owner:

  • Hooman Kamel - Mid-career Tenured Researcher, Cornell University

hma4

Project Purpose(s)

  • Disease Focused Research (cardiovascualr diseases, diabetes and dementia)
  • Population Health ...
  • Social / Behavioral
  • Educational
  • Drug Development
  • Methods Development
  • Control Set
  • Ancestry
  • Commercial
  • Ethical, Legal, and Social Implications (ELSI)

Scientific Questions Being Studied

Diabetes, cardiovascualar diseases and dementia are the major challenges to human health, which are determined by genetic susceptibility, environmental risk factors, and their interactions. However, the evidence on the G×E (genetic facotrs* environmental factors) interaction and unconfounded estimates of a modifiable exposure is still laking. This study plan aim to investigate whether modifiable factors for such disease may interact with the genetic variations in relation to risks of diabetes, cardiovascualar diseases and dementia.

Scientific Approaches

Datasets: all the genotype and phenotype related datasets
Reseach method:I plan to use G×E interaction , COX model, Losigical model in my study.
Tools: R.
Scientific question: whether modifiable factors could modify the association between genetic risk and disease risks (Diabetes, cardiovascualar diseases and dementia)

Anticipated Findings

Some individual environmental fators or an overall modifiable-risk-factor profile may modify the association between genetic risk and disease risk.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Hao Ma - Research Fellow, Tulane University

How to Backup Notebooks and Intermediate Results

Project Purpose(s)

  • Other Purpose (Demonstrate to workbench users how to create snapshots of notebooks and backups of intermediate results stored in other files such as plot images and derived data.) ...

Scientific Questions Being Studied

Not applicable - these utility notebooks do not perform any analyses.

Scientific Approaches

Not available.

Anticipated Findings

Not applicable - these utility notebooks do not perform any analyses.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Eric Song - Administrator, All of Us Program Operational Use

Collaborators:

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

HTN

Project Purpose(s)

  • Disease Focused Research (hypertension) ...

Scientific Questions Being Studied

What is the prevalence of hypertension (HTN) defined using an electronic health record definition from eMERGE among UBR groups defined by race/ethnicity, income and education?

Do treatment patterns for HTN (using medication sequencing analysis) vary by UBR groups defined by race/ethnicity, income and education, and in geographic regions based on grouping states?

Scientific Approaches

Not available.

Anticipated Findings

There may be disparities in HTN across racial and income groups of policy interest.

Demographic Categories of Interest

  • Income Level

Research Team

Owner:

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

Collaborators:

  • Elizabeth Karlson - Late Career Tenured Researcher, Massachusetts General Hospital
  • Francis Ratsimbazafy - Other, All of Us Program Operational Use
  • Andrea Ramirez - Other, All of Us Program Operational Use

HTN_stroke_race

Project Purpose(s)

  • Disease Focused Research (stroke)
  • Other Purpose (“This work is a result of an All of Us Research Program Demonstration Project. The projects are efforts by the Program designed to meet the program's goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. This work was reviewed and overseen by the All of Us Research Program Science Committee and the Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use”. ) ...

Scientific Questions Being Studied

Prior studies indicate that there are racial differences in the impact of elevated blood pressure on stroke risk. The exact blood pressure threshold at which to begin antihypertensive therapy remains controversial, as does the ideal blood pressure target. Regardless of the specific thresholds and targets chosen, current guidelines and clinical practice patterns do not account for race and ethnicity when managing blood pressure. However, prior studies indicate that there are racial differences in the impact of elevated blood pressure on stroke risk; in a large, longitudinal cohort study, every 10-mm Hg increase in systolic blood pressure was associated with an 8% increase in stroke risk among white individuals versus a 24% increase in black individuals. These findings suggest that blood pressure targets may need to be personalized, at least based on race/ethnicity and ideally based on genetics, vascular risk factors, and lifestyle factors.

Scientific Approaches

The study population comprised 108,322 participants with a SBP measurement, of whom 369 had stroke before/after the measurement. In an unadjusted logistic regression model, systolic blood pressure was significantly associated with stroke (OR per mm Hg, 1.01; 95% CI, 1.00-1.01; P < 0.001). This confirms a well-established finding from numerous prior studies. We then examined this association stratified by black versus non-black. Among black participants, SBP was significantly associated with stroke (OR per mm Hg, 1.01; 95% CI, 1.00-1.02; P = 0.002); in patients of other races, SBP was non-significantly associated with stroke (OR per mm Hg, 1.00; 95% CI, 1.00-1.01; P = 0.11). The lack of association in non-black participants is most likely due to insufficient power, but the different strength of association between black and non-black participants confirms prior findings in other cohorts such as REGARDS.

Anticipated Findings

That the association between hypertension and stroke is stronger among African Americans compared to patients of other races. These results suggest that ethnicity-specific blood pressure thresholds may be superior to a uniform population-wide threshold and promises to inform current uncertainties about blood pressure management.

Demographic Categories of Interest

  • Race / Ethnicity

Research Team

Owner:

  • Cenai Zhang - Project Personnel, Cornell University

Collaborators:

  • Margaret Ross - Late Career Tenured Researcher, Cornell University
  • Hooman Kamel - Mid-career Tenured Researcher, Cornell University

Hypertension

Project Purpose(s)

  • Population Health
  • Other Purpose (This work is a result of an All of Us Research Program Demonstration Project. The projects are efforts by the Program designed to meet the program's goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. This work was reviewed and overseen by the All of Us Research Program Science Committee and the Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use) ...

Scientific Questions Being Studied

Uncontrolled hypertension is a primary contributor to coronary heart disease, stroke, and heart failure. Hypertension can be treated successfully in many cases with medication and prevented or delayed with lifestyle modifications. Even with this success, the prevalence of hypertension continues to be at levels of public health concern, and its control in the United States is far below what is possible. In this demonstration project, we focus on the prevalence of hypertension and its awareness, treatment, and control in a large and diverse participant sample of the All of Us Research Program. Specific questions include:
1) What is the prevalence of hypertension among participants in the All of Us Research Program?
2) Among hypertensive participants, what is the prevalence of awareness, treatment, and control?
3) How do these estimates compare to the general US population assessed in the National Health and Nutrition Examination Survey (NHANES), 2015-2016?

Scientific Approaches

This descriptive analysis is based on blood pressure measurements from the participants’ physical measurement evaluations, and data derived from participant provided information (PPI) and electronic health records (EHR).
1) Demographic factors such as age, sex, race/ethnicity, educational attainment, income and health insurance were assessed in the PPI questionnaire.
2) PPI questionnaire data was also used to define self-reported doctor diagnosis of hypertension and self-reported hypertension medication use.
3) EHR evidence of hypertension diagnosis was defined as the presence of ICD9/ICD10 codes corresponding to hypertension any time before baseline.
4) EHR evidence of hypertension medication use was defined as at least one drug exposure to hypertension medications any time before baseline.

Anticipated Findings

For this study, we anticipate that the prevalence, awareness, treatment, and control of hypertension will be different across demographic strata. This will help to identify health disparities and improve health equity in vulnerable populations. We also anticipate that estimates will be different between the All of Us Research Program and the general US population assessed in NHANES 2015-2016. Understanding these differences will help to characterize potential selection bias and demonstrate the quality and utility of the All of Us data and tools.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Madhawa Saranadasa - Graduate Trainee, University of Illinois at Chicago

Collaborators:

  • Maria Argos - Mid-career Tenured Researcher, University of Illinois at Chicago

Hypertension Cost Effectiveness

Project Purpose(s)

  • Disease Focused Research (essential hypertension)
  • Population Health ...
  • Drug Development
  • Methods Development

Scientific Questions Being Studied

We want conduct an exhaustive cost-effectiveness analysis on all antihypertensive drugs across a variety of outcomes to be able to suggest modified prescription guidelines. Hypertension affects 30-45% of adults in the United States (depending on the definition of hypertension used) representing a significant economic burden on both individuals and society. However, there are many guidelines spanning different countries, associations, and organizations which often recommend multiple drug classes even in the case of another existing condition (e.g. type 2 diabetes). We want to use real world evidence to conduct cost effectiveness analysis to improve the existing guidelines.

Scientific Approaches

We plan to use the electronic health records (EHR) data as real world evidence to conduct a cost-effectiveness analysis. Specifically, we will get the EHR of all individuals who have been diagnosed with hypertension and are first time users of antihypertensive monotherapies. We will then match the cohorts with propensity score matching and conduct cost-effectiveness analysis on all pairwise combinations of drugs with respect to a number of outcomes including: QALY and ICER.

Anticipated Findings

We anticipate that for most disease/secondary outcomes there is no major difference between different classes or active ingredients of antihypertensives especially for the short term outcome of 5 years. With our findings, we will be able to suggest changes to the existing hypertension prescription guidelines for different races, sexes, ages, preexisting diseases (e.g. chronic kidney disease, type 2 diabetes, etc.) which we hope will have a positive impact on healthcare.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Douglas Arneson - Research Fellow, University of California, San Francisco

Hypertension Cost Effectiveness

Project Purpose(s)

  • Disease Focused Research (essential hypertension)
  • Population Health ...
  • Drug Development
  • Methods Development

Scientific Questions Being Studied

We want conduct an exhaustive cost-effectiveness analysis on all antihypertensive drugs across a variety of outcomes to be able to suggest modified prescription guidelines. Hypertension affects 30-45% of adults in the United States (depending on the definition of hypertension used) representing a significant economic burden on both individuals and society. However, there are many guidelines spanning different countries, associations, and organizations which often recommend multiple drug classes even in the case of another existing condition (e.g. type 2 diabetes). We want to use real world evidence to conduct cost effectiveness analysis to improve the existing guidelines.

Scientific Approaches

We plan to use the electronic health records (EHR) data as real world evidence to conduct a cost-effectiveness analysis. Specifically, we will get the EHR of all individuals who have been diagnosed with hypertension and are first time users of antihypertensive monotherapies. We will then match the cohorts with propensity score matching and conduct cost-effectiveness analysis on all pairwise combinations of drugs with respect to a number of outcomes including: QALY and ICER.

Anticipated Findings

We anticipate that for most disease/secondary outcomes there is no major difference between different classes or active ingredients of antihypertensives especially for the short term outcome of 5 years. With our findings, we will be able to suggest changes to the existing hypertension prescription guidelines for different races, sexes, ages, preexisting diseases (e.g. chronic kidney disease, type 2 diabetes, etc.) which we hope will have a positive impact on healthcare.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Douglas Arneson - Research Fellow, University of California, San Francisco

Hypertension Cost Effectiveness

Project Purpose(s)

  • Disease Focused Research (essential hypertension)
  • Population Health ...
  • Drug Development
  • Methods Development

Scientific Questions Being Studied

We want conduct an exhaustive cost-effectiveness analysis on all antihypertensive drugs across a variety of outcomes to be able to suggest modified prescription guidelines. Hypertension affects 30-45% of adults in the United States (depending on the definition of hypertension used) representing a significant economic burden on both individuals and society. However, there are many guidelines spanning different countries, associations, and organizations which often recommend multiple drug classes even in the case of another existing condition (e.g. type 2 diabetes). We want to use real world evidence to conduct cost effectiveness analysis to improve the existing guidelines.

Scientific Approaches

We plan to use the electronic health records (EHR) data as real world evidence to conduct a cost-effectiveness analysis. Specifically, we will get the EHR of all individuals who have been diagnosed with hypertension and are first time users of antihypertensive monotherapies. We will then match the cohorts with propensity score matching and conduct cost-effectiveness analysis on all pairwise combinations of drugs with respect to a number of outcomes including: QALY and ICER.

Anticipated Findings

We anticipate that for most disease/secondary outcomes there is no major difference between different classes or active ingredients of antihypertensives especially for the short term outcome of 5 years. With our findings, we will be able to suggest changes to the existing hypertension prescription guidelines for different races, sexes, ages, preexisting diseases (e.g. chronic kidney disease, type 2 diabetes, etc.) which we hope will have a positive impact on healthcare.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Douglas Arneson - Research Fellow, University of California, San Francisco

Hypertension Cost Effectiveness

Project Purpose(s)

  • Disease Focused Research (essential hypertension)
  • Population Health ...
  • Drug Development
  • Methods Development

Scientific Questions Being Studied

We want conduct an exhaustive cost-effectiveness analysis on all antihypertensive drugs across a variety of outcomes to be able to suggest modified prescription guidelines. Hypertension affects 30-45% of adults in the United States (depending on the definition of hypertension used) representing a significant economic burden on both individuals and society. However, there are many guidelines spanning different countries, associations, and organizations which often recommend multiple drug classes even in the case of another existing condition (e.g. type 2 diabetes). We want to use real world evidence to conduct cost effectiveness analysis to improve the existing guidelines.

Scientific Approaches

We plan to use the electronic health records (EHR) data as real world evidence to conduct a cost-effectiveness analysis. Specifically, we will get the EHR of all individuals who have been diagnosed with hypertension and are first time users of antihypertensive monotherapies. We will then match the cohorts with propensity score matching and conduct cost-effectiveness analysis on all pairwise combinations of drugs with respect to a number of outcomes including: QALY and ICER.

Anticipated Findings

We anticipate that for most disease/secondary outcomes there is no major difference between different classes or active ingredients of antihypertensives especially for the short term outcome of 5 years. With our findings, we will be able to suggest changes to the existing hypertension prescription guidelines for different races, sexes, ages, preexisting diseases (e.g. chronic kidney disease, type 2 diabetes, etc.) which we hope will have a positive impact on healthcare.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Douglas Arneson - Research Fellow, University of California, San Francisco

Hypertensive Disorders of Pregnancy

Project Purpose(s)

  • Disease Focused Research (Hypertensive disorder of pregnancy)
  • Social / Behavioral ...
  • Other Purpose (This work is a result of an All of Us Research Program Demonstration Project. The projects are efforts by the Program designed to meet the program's goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. This work was reviewed and overseen by the All of Us Research Program Science Committee and the Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use.)

Scientific Questions Being Studied

1. What is the prevalence of hypertensive disorders during pregnancy?
2. What is the prevalence of hypertensive disorders during pregnancy by demographics? Of those diagnosed with a hypertensive disorder during pregnancy, what is the epidemiology of the risk factors associated with hypertension in pregnancy?
3. Are there racial disparities in hypertension during pregnancy, when adjusted for these risk factors?
4. How can one use heterogeneous data sources within the All of Us dataset to explore disease associations using self-reported exposures (Participant Provided Information, or “PPI”) and exposures captured in the electronic medical record (EHR).”

Scientific Approaches

Our sample was pulled from the 78,938 females in the AoU cohort who had EHR and PPI data. Females were identified as participants with female sex assigned at birth. Of these, only the 13,155 females who had at least 1 SNOMED code in their EHR as "pregnancy finding" were included in the analysis. For our analyses, a participant was classified as having a hypertensive disorder of pregnancy if they had at least one SNOMED code for gestational hypertension, pre-eclampsia with or without severe features, eclampsia, or HELLP Syndrome. We used published risk factors for preeclampsia as described by the United States Preventive Services Task Force in our univariate and multivariate analysis. Odds ratios were calculated for the risk factors. Descriptive statistics for the overall pregnant female cohort and the hypertensive disorder of pregnancy cohort were also classified. We used both EHR and PPI data to identify the risk factors for hypertensive disorders of pregnancy.

Anticipated Findings

We anticipate to see racial disparities in the prevalence of hypertensive disorders during pregnancy. Similar to previous literature, we anticipate our results will show participants who identify as African American are at greater odds of being diagnosed with hypertensive disorder of pregnancy compared to White participants. We also anticipate finding higher odds of being diagnosed with hypertensive disorders of pregnancy among participants who have at least one risk factor for preeclampsia as described by USPSTF compared to participants without any risk factors. This study will serve to demonstrate the quality, utility, and diversity of the All of Us data and tools, providing researchers options for study design and validation.

Demographic Categories of Interest

  • Race / Ethnicity
  • Sex at Birth
  • Access to Care
  • Education Level
  • Income Level

Research Team

Owner:

  • Lizette Mendez - Project Personnel, Boston Medical Center

Collaborators:

  • Roxana Loperena Cortes - Other, All of Us Program Operational Use
  • Francis Ratsimbazafy - Other, All of Us Program Operational Use
  • Nyia Noel - Mid-career Tenured Researcher, Boston Medical Center
  • Confidence Achilike - Project Personnel, Boston Medical Center
  • Guohai Zhou - Early Career Tenure-track Researcher, Massachusetts General Hospital

Hypoglycemia in Non-Diabetics

Project Purpose(s)

  • Disease Focused Research (hypoglycemia)
  • Educational ...

Scientific Questions Being Studied

Hypoglycemia is a common occurrence in hospitalized patients with diabetes and is associated with adverse clinical outcomes. Numerous prospective and retrospective studies demonstrate an increase risk of cardiovascular events, all-cause hospitalization, longer hospital stay, and all-cause mortality among diabetic patients who have experienced hypoglycemia during inpatient admissions versus those who have not. In those without diabetes, inpatient hypoglycemia may still occur. Studies demonstrate that even in patients without diabetes, hypoglycemia results in poor clinical outcomes as related to mortality and cognitive function. There is no standard protocol for blood glucose monitoring inpatient for patients without diabetes. A standardized protocol could more closely trend blood glucose values among hospitalized non-diabetic patients who have an elevated risk of hypoglycemia in order to reduce the rate of hypoglycemia and its related complications.

Scientific Approaches

A retrospective review of patients will be conducted who have experienced at least one episode of hypoglycemia (BG < 70 mg/dL) during inpatient hospitalization and potential risk factors which may have contributed to such episode will be identified. Examples include end stage liver disease, renal disease, cardiac disease, protein-calorie malnutrition. Following, use these risk factors in a multivariate analysis to create a scoring system which assigns specific point values to each risk factor in order to predict risk of hypoglycemia during admission.

Anticipated Findings

The findings should create a model of risk factors for hypoglycemia among hospitalized non-diabetics. Using this risk model, other researchers may be able to expand the findings to create prospective studies that aim to reduce the risk of developing hypoglycemia groups with these risk factors.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Anand Gandhi - Research Associate, Banner Health

Hypothesis generation

Project Purpose(s)

  • Population Health
  • Methods Development ...

Scientific Questions Being Studied

The main focus is to study patterns and trends in individuals taking complementary medicines and interventions, as well as associations of these interventions with multi-health system outcomes. We hope to use study findings to generate hypotheses for planning of clinical trials in whole-person health research.
Specific questions include:
1) What are the patterns and trends in complementary medicines such as yoga, acupuncture, mindfulness interventions etc.? What are the participants’ demographic characteristics and health/comorbidity conditions associated with combinations of interventions?
2) What are the associations between complementary medicines and behavioral interventions and health outcomes? Specifically, are there certain combinations of interventions associated with great improvement of multi-health system outcomes?
3) Are there latent traits in individuals associated with great multi-health system outcomes related to complementary medicines and interventions?

Scientific Approaches

We plan to apply statistical methods for longitudinal data and machine learning to identify patterns, trends and associations. R will be used for all analyses. Specifically, we will:
1) Construct datasets with individuals that have measures of complementary medicines and behavioral interventions, and/or have provided answers to survey questions related to those interventions.
2) Use growth mixture modeling to identify sub-populations, to describe longitudinal change within each sub-population, and to examine differences in trends with respect to use of those interventions over time.
3) Use structural equation modeling to link identified sub-populations and trends to individual level health-related outcomes and study the associations.
4) Also use clustering techniques to explore cross-sectional data to identify combinations of individual characteristics associated with great improvement of multi-health system outcomes related to complementary medicines and behavioral interventions.

Anticipated Findings

From this study we expect to:
1) Identify patterns and trends of complementary medicines and behavioral interventions applications.
2) Build constructs that characterize underlying traits of whole-person health.
3) Identify traits in individuals associated with great improvement of multi-health system outcomes related to complementary medicines and behavioral interventions.
Study findings will provide important information in understanding the causal relationship between complementary interventions and health outcomes, and will be used to generate hypotheses of effectiveness of a combination of behavioral and other interventions on whole person health, which can be tested in future clinical trials.

Demographic Categories of Interest

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

Research Team

Owner:

  • Qilu Yu - Senior Researcher, NIH

IBD exploration

Project Purpose(s)

  • Disease Focused Research (inflammatory bowel disease) ...

Scientific Questions Being Studied

Inflammatory Bowel Disease is a chronic immune-mediated disease affecting 1 in 200 in the US at a cost of $15-32B annually. Once thought to be a disease of the western world, IBD is now recognized as affecting all people around the globe. IBD continues to rise in incidence and prevalence worldwide, yet its underlying cause remains unknown. Moreover, current treatment approaches largely rely on a treat-and-see approach rather than one that optimally matches treatments to patients, leading to unnecessary therapeutic risks and healthcare costs.

Scientific Approaches

Our objective is to use a data-integrative approach in order to identify risk factors for IBD incidence and complications, as well as predictors of treatment response. We will use a combination of supervised and unsupervised machine learning methods in order to identify these risk factors. Following an exploration of the data assets native to this cohort, we may ultimately decide to incorporate data from other sources (GEO, dbGAP, de-identified EHR data from elsewhere) in order to maximize the power of the model to identify these preclinical signals.

We will use a variety of sensitivity analyses to test the robustness of our findings. For example, given that IBD is a disease that can take years to diagnose, we will explore analyses that intentionally use a longer look back period in the hopes of identifying causative factors that truly identify silent/pre-clinical disease.

Anticipated Findings

The anticipated findings include the identification several risk factors (clinical and genetic), as well as predictors of treatment response. This would aid researchers in better understanding the cause of IBD and more precise approaches to treatment.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Vivek Rudrapatna - Early Career Tenure-track Researcher, University of California, San Francisco

Improving the estimation of CVD risk among CKD patients

Project Purpose(s)

  • Disease Focused Research (chronic kidney failure) ...

Scientific Questions Being Studied

It is well known that there is a strong association between Chronic Kidney Disease (CKD), and Cardiovascular Diseases (CVD). Although there are several CVD prediction scores available to predict the probability of CVD in the general population, none of them are accurate enough to estimate the extent of CVD risk in CKD patients. For example, the Framingham score has been questioned as it was created based on a homogenous, geographically restricted and predominantly white population. Additionally, there are other factors (non-traditional risk factors of CVD that are specific to CKD such as albuminuria, anemia, fluid overload etc.) which are not included in the Framingham score which may play an essential role in estimating ischemic heart disease in patients with CKD. As a result, it may overestimate the risk and have poor discriminatory power in the CKD population. All these indicate the need for the development of more appropriate risk factors to assess the CVD risk among CKD patients.

Scientific Approaches

• A retrospective cohort study will be conducted to create a model capable of predicting CVD using traditional and non-traditional risk factors of CVD in patients with CKD. The study will include CVD patients with CKD: age 18-80, CKD stage 1-5(including patients on dialysis), in accordance with the Kidney Disease Outcomes Quality Initiative (KDOQI).
• The baseline data collection will include but not limited to age, sex, CKD stage, CVD’s history, medications history as well as traditional CVD risk factors.
• Baseline renal functions and other parameters related to CVD will be assessed and those include: glomerular filtration rate (GFR), serum creatinine, serum cystatin C, low-density cholesterol (LDL), high-density cholesterol (HDL), total cholesterol and high-sensitivity C-reactive protein (CRP).
• To investigate the association between CVD in CKD patients and potential risk factors, Cox Proportional-Hazards model will be used for the statistical analysis.

Anticipated Findings

• Creating a model using traditional risk factors (hypertension, diabetes at el) and non-traditional risk factors (albuminuria, anemia at el) can estimate the risk of CVD in patients with CKD. This model would help to adequately treat CVD and prevent worsening of CVD in patients with CKD

Demographic Categories of Interest

  • Race / Ethnicity
  • Age

Research Team

Owner:

  • Dayanjan Wijesinghe - Early Career Tenure-track Researcher, Virginia Commonwealth University

Collaborators:

  • Saud Alshammari - Graduate Trainee, Virginia Commonwealth University
  • Joshua Morriss - Graduate Trainee, Virginia Commonwealth University
  • Daniel Contaifer Junior - Project Personnel, Virginia Commonwealth University
  • VIRGINIA UNIVERSITY - Graduate Trainee, Virginia Commonwealth University

Initial Testing Workspace

Project Purpose(s)

  • Other Purpose (An initial testing workspace to get oriented and develop further thoughts on usage of the data. ) ...

Scientific Questions Being Studied

An initial testing workspace to get oriented and develop further thoughts on usage of the data.

Scientific Approaches

An initial testing workspace to get oriented and develop further thoughts on usage of the data.

Anticipated Findings

An initial testing workspace to get oriented and develop further thoughts on usage of the data.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Allison Heath - Other, Children's Hospital of Philadelphia