Research Projects Directory

Alzheimer's Disease Age of Diagnosis

I am interested in investigating if the age of diagnosis of Alzheimer's disease can be predicted from various social determinants of health and healthcare utilization. This question is important because it will provide valuable insights into factors that contribute to…

Scientific Questions Being Studied

I am interested in investigating if the age of diagnosis of Alzheimer's disease can be predicted from various social determinants of health and healthcare utilization. This question is important because it will provide valuable insights into factors that contribute to the delay of diagnosis for Alzheimer's Disease. Early detection and treatment is important for improved quality of life and prognosis.

Project Purpose(s)

Social / Behavioral

Scientific Approaches

I will need access to the conditions, social determinants, and healthcare utilization, and potentially a few other datasets from All of Us. I will be trying a variety of machine learning techniques to estimate the age of diagnosis based on the other variables collected. Python will be used to analyze the data.

Anticipated Findings

I anticipate that those with lower levels of healthcare utilization and lower levels of social support will experience older ages of diagnosis. This will add more support and evidence for the need for access to primary care in the early detection of Alzheimer's Disease.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Mackenzie Kramer - Graduate Trainee, Clemson University

Food insecurity and Sleep

Sleep health is an essential component of overall health, particularly among older adults. Insufficient sleep has been associated with cognitive impairment and degeneration as well as an increased risk of developing dementia and Alzheimer's disease. Among older adults, biological changes…

Scientific Questions Being Studied

Sleep health is an essential component of overall health, particularly among older adults. Insufficient sleep has been associated with cognitive impairment and degeneration as well as an increased risk of developing dementia and Alzheimer's disease. Among older adults, biological changes contribute to sleep difficulties and shorter sleep duration. Older adults who belong to racial/ethnic minority groups and those with low socioeconomic status experience worse sleep outcomes than their non-Latino White and higher socioeconomic counterparts. Social factors may be driving these sleep health disparities, however, research in this area is limited. This study will examine the association between food insecurity, housing security, and sleep health among older adults. Addressing social determinants of sleep health could inform policy-level interventions that help address racial/ethnic and socioeconomic sleep health disparities which contribute to other health disparities among these groups.

Project Purpose(s)

Social / Behavioral

Scientific Approaches

This study will use survey data from the social determinants of health surveys and Fitbit data to examine the association between food insecurity, housing insecurity, and sleep health outcomes among older adults. We will use linear regressions to test these associations controlling for age, gender, language of survey administration, nativity status, and BMI. Individuals with a diagnosis of a sleep disorder will be excluded from the sample. We will also test the moderating effect of living in a rural vs. urban/suburban region of the US using cross-products.

Anticipated Findings

We hypothesize that food insecurity and housing insecurity will be positively associated with short sleep duration. These findings will contribute to the growing scientific knowledge of the social determinants of sleep health and will address a research gap in sleep research among racial/ethnic and low socioeconomic status older adults living in the US.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Luciana Giorgio - Early Career Tenure-track Researcher, University of Alabama

cardiovascular_phenotypes

Are there novel variants associated with cardiovascular disease phenotypes we can pick up using a larger, more diverse dataset? Do these variants overlap with any cognitive phenotypes like Alzheimer's Disease, Parkinson's Disease, etc?

Scientific Questions Being Studied

Are there novel variants associated with cardiovascular disease phenotypes we can pick up using a larger, more diverse dataset? Do these variants overlap with any cognitive phenotypes like Alzheimer's Disease, Parkinson's Disease, etc?

Project Purpose(s)

Disease Focused Research (Cardiovascular diseases)

Scientific Approaches

I plan to use genome-wide association studies (GWAS) vis PLINK and SAIGE. I will create cohorts based on the appearance of certain ICD10-based codes in patients' EHR for cardiovascular phenotypes.

Anticipated Findings

We anticipate finding novel variants linked to cardiac disease and that some may overlap with cognitive phenotypes as well.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Scott Dudek - Project Personnel, University of Pennsylvania
Anni Moore - Graduate Trainee, University of Pennsylvania

Testing cardiac phenotypes

Are there novel variants associated with cardiovascular disease phenotypes we can pick up using a larger, more diverse dataset? Do these variants overlap with any cognitive phenotypes like Alzheimer's Disease, Parkinson's Disease, etc?

Scientific Questions Being Studied

Are there novel variants associated with cardiovascular disease phenotypes we can pick up using a larger, more diverse dataset? Do these variants overlap with any cognitive phenotypes like Alzheimer's Disease, Parkinson's Disease, etc?

Project Purpose(s)

Disease Focused Research (Cardiovascular diseases)

Scientific Approaches

I plan to use genome-wide association studies (GWAS) via PLINK and SAIGE. I will create cohorts based on the appearance of certain ICD10-based codes in patients' EHR for cardiovascular phenotypes.

Anticipated Findings

We anticipate finding novel variants linked to cardiac diseases and that some may overlap with cognitive phenotypes as well.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Anni Moore - Graduate Trainee, University of Pennsylvania

Nikhil Controlled Tier Workspace

Exploring possible project ideas for master's capstone and projects, potentially including the effect of sildenafil/aspiring/other blood brain barrier crossing drugs on cognitive decline/Alzheimer's. However, I am primarily seeking out the limitations of the AOU workbench as part of a class.

Scientific Questions Being Studied

Exploring possible project ideas for master's capstone and projects, potentially including the effect of sildenafil/aspiring/other blood brain barrier crossing drugs on cognitive decline/Alzheimer's. However, I am primarily seeking out the limitations of the AOU workbench as part of a class.

Project Purpose(s)

Educational

Scientific Approaches

Creating a table 1 for the BBB-crossing drugs and neurodegeneration project discussed previously. In addition, seeking to find other correlations with neurological diseases.

Anticipated Findings

There is not necessarily an anticipated finding since there is so much contrary findings on the subject matter. I hope to use the AOU dataset to add to existing research.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Nikhil Etikela - Graduate Trainee, University of Chicago
Andrew Craver - Project Personnel, University of Chicago

Comorbidity in MS

Alzheimer's disease (AD) and neuroimmunological disorders are debilitating neurological disorders that pose significant challenges to public health. Over recent decades, life expectancy for individuals with neuroimmunological conditions has increased, leading to co-occurring AD as evidenced by descriptive epidemiological studies (case…

Scientific Questions Being Studied

Alzheimer's disease (AD) and neuroimmunological disorders are debilitating neurological disorders that pose significant challenges to public health. Over recent decades, life expectancy for individuals with neuroimmunological conditions has increased, leading to co-occurring AD as evidenced by descriptive epidemiological studies (case reports and case series). However, AD is seldom studied as a comorbid condition with autoimmune neurological disorders in analytical epidemiology, leaving the demographic, clinical, and neuropathological aspects of AD in people with neuroimmunological disorders poorly understood. Given preliminary evidence of an elevated AD risk in people with neuroimmune diseases, it is crucial to validate this association using comprehensive AD coding algorithms across diverse populations with larger sample sizes.

Project Purpose(s)

Disease Focused Research (neuroimmune disorders)
Population Health
Drug Development
Ancestry

Scientific Approaches

A matched case-control design will be used. I will first identify cases (people with autoimmune neurological diseases), and match them to controls who don't have neuroimmune disorders. Then I will compare prevalence and incidence of AD between cases and controls. Subsequently, I will perform analysis in cases to identify risk factors (socioeconomic status, disease characteristics, health behaviors, and health comorbidities) that are associated with AD development.

Anticipated Findings

We hypothesize that people with neuroimmunological diseases are more likely to develop AD than healthy controls. In addition, factors such as demographics, social determinants of health, disease characteristics, health behaviors, and comorbidities are associated with AD development in people with neuroimmunological conditions. We anticipate to provide statistics (incidence and prevalence) on the coexistence of AD and neuroimmunological disorders. We also anticipate to validate previously observed associations using comprehensive AD coding algorithms across diverse populations with larger sample sizes. Lastly, we hope to provide insights in clinical intervention for AD prevention and comorbidity management in people with autoimmune neurological conditions.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Chen Hu - Graduate Trainee, University of Pittsburgh

UCSD/ Oslo Group

Non-invasive and low-cost tools for early detection of individuals at high risk for developing Alzheimer’s Disease (AD) are of crucial importance. While genetic studies (GWAS) exist, most focus on European populations, limiting their application to diverse groups. Given the Eurocentric…

Scientific Questions Being Studied

Non-invasive and low-cost tools for early detection of individuals at high risk for developing Alzheimer’s Disease (AD) are of crucial importance. While genetic studies (GWAS) exist, most focus on European populations, limiting their application to diverse groups. Given the Eurocentric biases in GWASs, PRSs are better at predicting AD risk for European ancestry as opposed to others. This research addresses this gap by developing a multimodal hazard score (MHS) that incorporates ethnically and genetically diverse populations. Our team established a successful polygenic hazard score (PHS) that predicts AD onset in European cohorts. Higher PHS predicted greater cognitive decline in CN, entorhinal cortex volume loss and predicted conversion from cognitively normal (CN), to mild cognitive impairment (MCI) to AD. This suggests its potential as a robust genetic risk indicator to be included in the MHS while also looking into diverse populations.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)

Scientific Approaches

Leveraging data from the different AD cohorts, we will conduct Cox proportional models to develop the MHS combining age, PHS, brain atrophy, biofluid-based data, and clinical outcomes to predict neurocognitive decline trajectory. This score will be able to predict AD progression and identify individuals at high risk of transitioning from cognitive normalcy to mild cognitive impairment (MCI) and eventually AD in a generalizable population. Additionally, we will also compute power calculations to estimate required clinical trial sample sizes after hypothetical enrichment using the MHS. Finally, we aim to replicate these findings in other diverse cohorts to ensure broader applicability.

Anticipated Findings

We believe this MHS surpasses single-modal and European data-driven models in predicting AD for diverse populations. This could pave the way for earlier intervention, more efficient research, and ultimately, improved outcomes for individuals at risk of AD.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Xin Wang - Project Personnel, University of California, San Diego
Ole Andreassen - Late Career Tenured Researcher, University of Oslo
Iris Broce - Early Career Tenure-track Researcher, University of California, San Diego
Gisele Sanda - Project Personnel, University of California, San Diego
Elise Koch - Research Fellow, University of Oslo

Duplicate of Sleeping Disease and Alzheimer's Disease

Alzheimer's Disease is the most common form of dementia in the United States. This study means to identify phenotypic associations between AD & SD using machine learning (ML). The objective is to identify risk factors of mid-age and aging patients…

Scientific Questions Being Studied

Alzheimer's Disease is the most common form of dementia in the United States. This study means to identify phenotypic associations between AD & SD using machine learning (ML). The objective is to identify risk factors of mid-age and aging patients with SD to develop AD.

Project Purpose(s)

Disease Focused Research (Alzheimer's Disease)

Scientific Approaches

We will use patient data within the observation window to build prediction model. We plan to use ML models including logistic regression, decision tree, and the advanced tree-based model XGboost. Data will be split into training and testing sets. Model hyperparameters will be tuned using a cross validation strategy in the training set.

Anticipated Findings

Based on the proposed study design, we expect to identify the specific phenotypic associations between Sleep Disorders (SD) and Alzheimer's Disease (AD) through the use of Machine Learning (ML) techniques. This will involve identifying various risk factors associated with mid-aged and older patients with SD that may predispose them to developing AD. Our models should illuminate individual-level outcomes for patients with AD, drawing on a range of structured patient records including demographic details, symptom profiles, comorbid conditions, medications, and Social Determinants of Health (SDoH). The outcomes from this study would not only deepen our understanding of the association between SD and AD but also provide practical tools and insights to help mitigate the impact of Alzheimer's disease.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Chang Su - Teacher/Instructor/Professor, Cornell University

Sleeping Disease & Alzheimer's Disease

Alzheimer's Disease is the most common form of dementia in the United States. This study means to identify phenotypic associations between AD & SD using machine learning (ML). The objective is to identify risk factors of mid-age and aging patients…

Scientific Questions Being Studied

Alzheimer's Disease is the most common form of dementia in the United States. This study means to identify phenotypic associations between AD & SD using machine learning (ML). The objective is to identify risk factors of mid-age and aging patients with SD to develop AD.

Project Purpose(s)

Disease Focused Research (Alzheimer's Disease)

Scientific Approaches

We will use patient data within the observation window to build prediction model. We plan to use ML models including logistic regression, decision tree, and the advanced tree-based model XGboost. Data will be split into training and testing sets. Model hyperparameters will be tuned using a cross validation strategy in the training set.

Anticipated Findings

Based on the proposed study design, we expect to identify the specific phenotypic associations between Sleep Disorders (SD) and Alzheimer's Disease (AD) through the use of Machine Learning (ML) techniques. This will involve identifying various risk factors associated with mid-aged and older patients with SD that may predispose them to developing AD. Our models should illuminate individual-level outcomes for patients with AD, drawing on a range of structured patient records including demographic details, symptom profiles, comorbid conditions, medications, and Social Determinants of Health (SDoH). The outcomes from this study would not only deepen our understanding of the association between SD and AD but also provide practical tools and insights to help mitigate the impact of Alzheimer's disease.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Chang Su - Early Career Tenure-track Researcher, Temple University
Chang Su - Teacher/Instructor/Professor, Cornell University

Duplicate of Oral Health and Dementia

Alzheimer's disease and related dementias (ADRD) is irreversible, progressive brain disease that affects 5.7 million Americans. It is the sixth leading cause of death among all adults and the fifth leading cause of death for those aged 65 or older.…

Scientific Questions Being Studied

Alzheimer's disease and related dementias (ADRD) is irreversible, progressive brain disease that affects 5.7 million Americans. It is the sixth leading cause of death among all adults and the fifth leading cause of death for those aged 65 or older. ADRD is devastating for individuals and families financially and emotionally. Identifying both individual and combined risk factors of ADRD and evaluating the impact of comorbidities on cognitive impairment is essential to improve cognitive health of older adults. Diabetes and poor oral health are common among older adults and both are risk factors for ADRD.

Project Purpose(s)

Population Health

Scientific Approaches

The proposed study is the first to examine the joint effect (additive or interactions) of both DM and poor oral health on ADRD and mortality, and the pathways from the co-occurrence to the onset of ADRD and mortality. Specific aims are: Aim 1: To examine the relationship between the co-occurrence of DM and poor oral health and the incidence of ADRD using a propensity matched sample within the study period. Aim 2: To examine the association between the co-occurrence of DM and poor oral health and the age of first diagnosis of ADRD among persons who developed ADRD during the study period. Aim 3: To test the pathways from the co-occurrence of DM and poor oral health to the onset of ADRD by examining the mediation effect of key mediating variables (i.e., CVD and stroke developed after baseline).

Anticipated Findings

The proposed study is the first to examine the effect of the co-occurrence of DM and poor oral health on ADRD and mortality, using large national samples. The proposed study will contribute to a better understanding of the risk profile of ADRD by providing important empirical evidence on the combined risk of both DM and oral health for ADRD. Further, it may identify modifiable factors that can serve as targets to reduce the risk of ADRD. The findings will have important implications for clinical practice and policy initiatives for integrating primary care and dental care.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Yaolin Pei - Research Fellow, New York University

Duplicate of Alzheimers_GWAS_Take5

What are the genetic determinants of Alzheimer's disease in the All of Us cohort? Can we use this data to further explain what predisposes individuals to the disease?

Scientific Questions Being Studied

What are the genetic determinants of Alzheimer's disease in the All of Us cohort? Can we use this data to further explain what predisposes individuals to the disease?

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)

Scientific Approaches

I will produce a genome wide association study using Regenie, after data curation using Plink. I will conduct this in a mixed-ancestry population to maximize initial statistical power before considering stratified analyses.

Anticipated Findings

I expect to reproduce some existing AD GWAS findings, demonstrating how they have relevance to a more diverse cohort.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Mohammad Waqas - Research Assistant, Mass General Brigham
Julian Willett - Research Fellow, Broad Institute
Julian Willett - Research Fellow, Mass General Brigham
Dmitry Prokopenko - Other, Mass General Brigham

Collaborators:

Jun Qian - Other, All of Us Program Operational Use

Duplicate of Gene-Environment Interactions in Alzheimer’s Disease

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly…

Scientific Questions Being Studied

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly to the risk of developing LOAD, the impact of environmental factors on the disease remains multifaceted. We propose a research proposal that assesses gene-environment (G×E) interactions in LOAD to analyze All of Us data. We will test these hypotheses: 1) individuals with the same or lower genetic risks will face an increased risk for LOAD when modified by higher environmental risks (vulnerability), whereas those with the same or higher genetic risks will experience a reduced risk when influenced by lower environmental risks (resilience); 2) effects of genetic risks on LOAD will vary modified by midlife and later-life environmental risks across ethnoracial groups.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)
Population Health
Social / Behavioral
Ancestry

Scientific Approaches

Polygenic risk scores (PRSs) are well-established in LOAD studies and feature an estimate of an individual’s genetic liability to LOAD by aggregating genetic effects of single-nucleotide variants (SNVs), thus serving as a comprehensive score for genetic risks. We will examine the interactions between PRSs and each of environmental risk factors in LOAD. We will also use an item response theory (IRT)-based model to generate environmental risk scores (ERSs) and investigate the interactions between PRSs and midlife/later-life ERSs in LOAD across ethnoracial groups. We will utilize the All of Us genetic data and published GWAS summary statistics to construct LOAD PRSs and construct midlife and later-life ERSs based on various environmental indicators in the All of Us data, including social of determinants of health (e.g., education and incomes), physical conditions (e.g., hypertension, diabetes, and depression), and lifestyle (e.g., smoking, alcohol, and exercise) variables.

Anticipated Findings

Overall, we will examine one-by-one G×E interactions in LOAD, but also construct comprehensive scores, PRSs and midlife/later-life ERSs, for individuals across ethnoracial groups, collectively contributing to advancing our knowledge of G×E interactions on vulnerability and resilience to LOAD. Informed by data indicating an overall risk (PRS modified by ERS) for LOAD, this approach may enable clinicians and individuals to initiate disease screening and discuss life planning strategies. This study would also provide a crucial tool for informing the design and implementation of personalized therapeutic and preventative programs, enabling more precise and individualized approaches to the treatment and prevention of LOAD. This study not only enhances our understanding of progression of LOAD but also provides a foundation for developing targeted strategies to mitigate risk and bolster resilience, thereby advancing personalized interventions for diverse populations at different life stages.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Inori Tsuchiya - Project Personnel, University of Kentucky

Collaborators:

Yucong Sang - Project Personnel, University of Kentucky
Xian Wu - Research Fellow, University of Kentucky
Sydney Shafer - Graduate Trainee, University of Kentucky
Jordan Brown - Teacher/Instructor/Professor, University of Kentucky
JIng Zhang - Research Fellow, University of Kentucky
Hady Sabra - Graduate Trainee, University of Kentucky

0504

The projects are related to Alzheimer's disease and related dementia, machine learning approaches, social determinants of health, etc.

Scientific Questions Being Studied

The projects are related to Alzheimer's disease and related dementia, machine learning approaches, social determinants of health, etc.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)

Scientific Approaches

The projects are related to Alzheimer's disease and related dementia, machine learning approaches, social determinants of health, etc.

Anticipated Findings

The projects are related to Alzheimer's disease and related dementia, machine learning approaches, social determinants of health, etc.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Xiang Qi - Research Fellow, New York University

Duplicate of Alzheimer's drug repurposing validation

Alzheimer’s disease (AD) is the most common cause of dementia in the elderly population; however, limited treatment options currently exist. This study seeks to clinically validate drug repurposing candidates for Alzheimer's disease.

Scientific Questions Being Studied

Alzheimer’s disease (AD) is the most common cause of dementia in the elderly population; however, limited treatment options currently exist. This study seeks to clinically validate drug repurposing candidates for Alzheimer's disease.

Project Purpose(s)

Methods Development

Scientific Approaches

This study will use a retrospective cohort study design to compare prevalence of Alzheimer's disease in patients with and without exposure to drug repurposing candidates.

Anticipated Findings

Successfully validated repurposing candidates will represent high-priority drugs for further investigation in clinical trials.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Monika Grabowska - Graduate Trainee, Vanderbilt University

Duplicate of Duplicate of ALDH2 HLP

Alcohol consumption is a risk factor for many chronic diseases, including some cancers, type 2 diabetes, and Alzheimer’s disease. Individuals with a specific variant of the aldehyde dehydrogenase 2 gene (ALDH2*2) are at higher risk of many of these diseases.…

Scientific Questions Being Studied

Alcohol consumption is a risk factor for many chronic diseases, including some cancers, type 2 diabetes, and Alzheimer’s disease. Individuals with a specific variant of the aldehyde dehydrogenase 2 gene (ALDH2*2) are at higher risk of many of these diseases. Given that ALDH2*2 is the most common single genetic variation in humans and that more than half of all American adults drink alcohol, an opportunity is present for targeted chronic disease risk reduction in a large number of Americans. However, in order to design effective public health strategies, such as targeted intervention programs, a better understanding of current alcohol consumption behaviors and associated factors, overall and stratified by ALDH2 genotype, is needed. This study aims to characterize the alcohol consumption behaviors among participants in the All of Us Research Program and examine factors that may be related to the behaviors, overall and by ALDH2 genotype.

Project Purpose(s)

Population Health
Social / Behavioral
Ancestry

Scientific Approaches

We will analyze data from the All of Us Research Program database regarding alcohol consumption behaviors, ALDH2 genotype (rs671), demographics, personal and family health history, socioeconomic factors, lifestyle factors, and social determinants of health. All participants with informative data for rs671 will be included in the study. These data sets will be from surveys, physical measurements, and the genomic data set. Statistical analyses will be done using R or python. We will examine relationships between these factors and alcohol consumption using Fisher’s exact test (categorical variables) and the Kruskal-Wallis test (continuous variables), overall and stratified by ALDH2 genotype and potentially other factors, for example, demographics.

Anticipated Findings

We hypothesize that alcohol consumption behaviors will be associated with factors including demographics, personal and family health history, socioeconomic factors, lifestyle factors, and social determinants of health, with ALDH2 genotype and potentially other factors. While a limited number of U.S. studies among university students have shown that ALDH2*2 homozygotes tend to avoid alcohol, many ALDH2 heterozygotes do consume alcohol, albeit at lower levels. There have been no studies examining alcohol consumption behaviors by ALDH2 genotype conducted outside the university setting in the U.S. The All of Us Research Program presents a valuable source of data from study participants across the U.S. which would enable the study of alcohol consumption behaviors in the context of genomics.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Manxi Yang - Graduate Trainee, University of California, Irvine
Hannah Park - Other, University of California, Irvine
Danielle Forman - Other, University of California, Irvine

Collaborators:

Jacqueline Kim - Other, University of California, Irvine
Hester Nguyen - Undergraduate Student, University of California, Irvine

Lab_7_cohort

I aim to investigate risk factors of Alzheimer's disease to enhance our understanding of its pathogenesis and develop more effective therapeutic interventions, ultimately improving patient outcomes and reducing the burden of this debilitating condition on individuals and healthcare systems worldwide.

Scientific Questions Being Studied

I aim to investigate risk factors of Alzheimer's disease to enhance our understanding of its pathogenesis and develop more effective therapeutic interventions, ultimately improving patient outcomes and reducing the burden of this debilitating condition on individuals and healthcare systems worldwide.

Project Purpose(s)

Educational

Scientific Approaches

I want to use the Alzheimer's cohort and perform a simple exploratory analysis aiming to visualize gender, race, and age distributions in the population.

Anticipated Findings

There are no particular findings that I am anticipating, however, this data exploration will help us explore and build new hypotheses.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Ilia Buralkin - Graduate Trainee, Baylor College of Medicine

Collaborators:

Shamika Ketkar - Other, Baylor College of Medicine

Duplicate of hypertensionDementia

Objective: This study aims to quantify the link between antihypertensive therapies and Alzheimer's disease risk, expanding on the established relationship between primary hypertension and AD. Introduction: Previous research has identified primary hypertension as a key risk factor for AD. Effective…

Scientific Questions Being Studied

Objective: This study aims to quantify the link between antihypertensive therapies and Alzheimer's disease risk, expanding on the established relationship between primary hypertension and AD.

Introduction: Previous research has identified primary hypertension as a key risk factor for AD. Effective hypertension management may impact AD progression and cognitive decline.

Methods: Leveraging All of Us data, we'll longitudinally analyze individuals, stratifying by hypertension status. Those with primary hypertension will be categorized by antihypertensive drug class: Thiazide-type diuretics, Calcium channel blockers, ACE inhibitors, and ARBs. We'll assess cognitive performance trajectories, adjusting for confounders, precision variables, and time-varying variables influenced by prior interventions. The confounders we will incorporate into our models will be derived by searching a biomedical knowledge graph derived from both the literature and biomedical ontologies.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)
Population Health
Social / Behavioral
Drug Development
Methods Development
Control Set

Scientific Approaches

Research: Our inaugural All of Us study aims to conduct a scientifically rigorous retrospective case-control investigation. We've devised techniques to identify confounding variables for model integration.

Data: We intend to employ All of Us participant health data to explore the impact of hypertension treatments on AD risk for individuals aged 65+ with a 10+ year history. We'll consider:

Exposure: Hypertension treatments by drug class for new users, mitigating "time-zero bias" as much as possible.
Outcome: Dementia presence (AD, VaD, mixed, cerebrovascular dementia) using ICD-9//10 codes and Memantine, donepezil prescriptions.
Time-varying confounders: Cognitive performance, physical activity. Additional covariates: Age, sex, race/ethnicity, APOe2/3/4 status, social determinants of health, vascular comorbidities (stroke, heart attack), and other research factors (sleep apnea, vitamin D deficiency, COPD).

Tools: We will use marginal structural models for longitudinal data analysis.

Anticipated Findings

Conclusion: This comprehensive study aims to elucidate the differential effects of specific antihypertensive medication classes on the risk and progression dynamics of Alzheimer's disease and affiliated dementias. The outcomes promise to enhance our understanding of the intricate nexus between hypertension management and the evolution of dementia-related outcomes.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Scott Malec - Early Career Tenure-track Researcher, University of New Mexico and University of New Mexico Health Sciences Center
Lori Sloane - Project Personnel, University of New Mexico and University of New Mexico Health Sciences Center

Alzheimer’s Disease Data Builder and Prediction Model

Our study focuses on using machine learning to build an early predictive model of Alzheimer’s Disease (AD). Treatments of AD patients often fail due to the too-late administration of clinical intervention. Therefore, the early prediction of Alzheimer’s Disease is important…

Scientific Questions Being Studied

Our study focuses on using machine learning to build an early predictive model of Alzheimer’s Disease (AD). Treatments of AD patients often fail due to the too-late administration of clinical intervention. Therefore, the early prediction of Alzheimer’s Disease is important because it will help improve the early diagnosis of high-risk patients and the preventive care and interventions that follow.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)
Methods Development

Scientific Approaches

The datasets that we will use include the electronic health records (EHR) data for patients with Alzheimer’s disease and related dementias. We will also attempt to find other data modalities that can be integrated with EHR to improve the performance of our predictor.

Anticipated Findings

There is a complex relationship among different biomedical data modalities and by finding a bridge to connect these data, we can create predictive models of AD that are highly scalable, efficient, accurate, and interpretable.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

qiannan zhang - Research Fellow, Cornell University
Haoxuan Zhang - Graduate Trainee, Cornell University

Collaborators:

Wenxin Chen - Graduate Trainee, Cornell University
Taykhoom Dalal - Graduate Trainee, Cornell University

Evaluations of ADRD

Our study focuses on using machine learning to build an early predictive model of Alzheimer’s Disease and related Dementia (ADRD). Treatments of ADRD patients often fail due to the too-late administration of clinical intervention. Therefore, the early prediction of Alzheimer’s…

Scientific Questions Being Studied

Our study focuses on using machine learning to build an early predictive model of Alzheimer’s Disease and related Dementia (ADRD). Treatments of ADRD patients often fail due to the too-late administration of clinical intervention. Therefore, the early prediction of Alzheimer’s Disease is important because it will help improve the early diagnosis of high-risk patients and the preventive care and interventions that follow.

Project Purpose(s)

Disease Focused Research (Alzheimer’s Disease and Related Dementia)
Population Health
Control Set

Scientific Approaches

The datasets that we will use include the electronic health records (EHR) data for patients with Alzheimer’s disease and related dementias. We will also attempt to find other data modalities that can be integrated with EHR to improve the performance of our predictor.

Anticipated Findings

There is a complex relationship among different biomedical data modalities and by finding a bridge to connect these data, we can create predictive models of AD that are highly scalable, efficient, accurate, and interpretable.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

qiannan zhang - Research Fellow, Cornell University

AD project

I'm interested in factors that trigger Alzheimer's Disease, which has been found a efficacious treatment.

Scientific Questions Being Studied

I'm interested in factors that trigger Alzheimer's Disease, which has been found a efficacious treatment.

Project Purpose(s)

Educational

Scientific Approaches

I'm planning to use multivariable regression analysis to explore factors that might trigger Alzheimer's Disease.

Anticipated Findings

I'm expecting to see an association between environmental factors and Alzheimer's Disease. These findings will contribute to the prediction and prevention of Alzheimer's Disease.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Xinhui Yang - Graduate Trainee, Louisiana State University Health Sciences Center, New Orleans

SM APOE 2024

Variants in the Apolipoprotein E (APOE) have been shown to have differential risk for conditions such as Alzheimer's disease and cardiovascular disease. This workspace will assess the feasibility of a large APOE association study by summarizing the APOE genotypes, hypercholesterolemia…

Scientific Questions Being Studied

Variants in the Apolipoprotein E (APOE) have been shown to have differential risk for conditions such as Alzheimer's disease and cardiovascular disease. This workspace will assess the feasibility of a large APOE association study by summarizing the APOE genotypes, hypercholesterolemia and demographics.

Project Purpose(s)

Educational
Ancestry

Scientific Approaches

The two rsIDs for APOE genotyping (rs429358 and rs7412) will be extracted from AllOfUs Hail tables. Data will be summarized into the defined APOE genotypes (ε2, ε3, ε4) and associated with hypercholesterolemia status. Hypercholesterolemia will be defined by the SNOMED code "E78.0 - Pure hypercholesterolemia". Summary statistics for APOE association will be stratified by age, sex and declared race/ethnicity.

Anticipated Findings

The study hopes to find similar statics as has been found in the UKBiobank cohort, with the addition of higher diversity. The summary statistics of these findings will be used as preliminary data to propose a larger cohort study.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Sevda Molani - Research Fellow, Institute for Systems Biology

Collaborators:

Yein Jeon - Graduate Trainee, University of Washington

Alzheimer's

Perform a simple exploratory analysis on Alzheimer's cohort. This is part of coursework for a Genetics subject at Baylor College of Medicine.

Scientific Questions Being Studied

Perform a simple exploratory analysis on Alzheimer's cohort. This is part of coursework for a Genetics subject at Baylor College of Medicine.

Project Purpose(s)

Educational

Scientific Approaches

Not currently specified. This is simply a simple exploratory analysis for educational purposes. This is part of coursework for a Genetics subject at Baylor College of Medicine.

Anticipated Findings

None. This is simply a simple exploratory analysis for educational purposes. This is part of coursework for a Genetics subject at Baylor College of Medicine.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Jorge Botas - Other, Baylor College of Medicine

Sang's Duplicate of Gene-Environment Interactions in Alzheimer’s Disease

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly…

Scientific Questions Being Studied

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly to the risk of developing LOAD, the impact of environmental factors on the disease remains multifaceted. We propose a research proposal that assesses gene-environment (G×E) interactions in LOAD to analyze All of Us data. We will test these hypotheses: 1) individuals with the same or lower genetic risks will face an increased risk for LOAD when modified by higher environmental risks (vulnerability), whereas those with the same or higher genetic risks will experience a reduced risk when influenced by lower environmental risks (resilience); 2) effects of genetic risks on LOAD will vary modified by midlife and later-life environmental risks across ethnoracial groups.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)
Population Health
Social / Behavioral
Ancestry

Scientific Approaches

Polygenic risk scores (PRSs) are well-established in LOAD studies and feature an estimate of an individual’s genetic liability to LOAD by aggregating genetic effects of single-nucleotide variants (SNVs), thus serving as a comprehensive score for genetic risks. We will examine the interactions between PRSs and each of environmental risk factors in LOAD. We will also use an item response theory (IRT)-based model to generate environmental risk scores (ERSs) and investigate the interactions between PRSs and midlife/later-life ERSs in LOAD across ethnoracial groups. We will utilize the All of Us genetic data and published GWAS summary statistics to construct LOAD PRSs and construct midlife and later-life ERSs based on various environmental indicators in the All of Us data, including social of determinants of health (e.g., education and incomes), physical conditions (e.g., hypertension, diabetes, and depression), and lifestyle (e.g., smoking, alcohol, and exercise) variables.

Anticipated Findings

Overall, we will examine one-by-one G×E interactions in LOAD, but also construct comprehensive scores, PRSs and midlife/later-life ERSs, for individuals across ethnoracial groups, collectively contributing to advancing our knowledge of G×E interactions on vulnerability and resilience to LOAD. Informed by data indicating an overall risk (PRS modified by ERS) for LOAD, this approach may enable clinicians and individuals to initiate disease screening and discuss life planning strategies. This study would also provide a crucial tool for informing the design and implementation of personalized therapeutic and preventative programs, enabling more precise and individualized approaches to the treatment and prevention of LOAD. This study not only enhances our understanding of progression of LOAD but also provides a foundation for developing targeted strategies to mitigate risk and bolster resilience, thereby advancing personalized interventions for diverse populations at different life stages.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Yucong Sang - Project Personnel, University of Kentucky
Xian Wu - Research Fellow, University of Kentucky
JIng Zhang - Research Fellow, University of Kentucky

Collaborators:

Inori Tsuchiya - Project Personnel, University of Kentucky

Jing_Duplicate Gene-Environment Interactions in Alzheimer’s Disease

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly…

Scientific Questions Being Studied

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly to the risk of developing LOAD, the impact of environmental factors on the disease remains multifaceted. We propose a research proposal that assesses gene-environment (G×E) interactions in LOAD to analyze All of Us data. We will test these hypotheses: 1) individuals with the same or lower genetic risks will face an increased risk for LOAD when modified by higher environmental risks (vulnerability), whereas those with the same or higher genetic risks will experience a reduced risk when influenced by lower environmental risks (resilience); 2) effects of genetic risks on LOAD will vary modified by midlife and later-life environmental risks across ethnoracial groups.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)
Population Health
Social / Behavioral
Ancestry

Scientific Approaches

Polygenic risk scores (PRSs) are well-established in LOAD studies and feature an estimate of an individual’s genetic liability to LOAD by aggregating genetic effects of single-nucleotide variants (SNVs), thus serving as a comprehensive score for genetic risks. We will examine the interactions between PRSs and each of environmental risk factors in LOAD. We will also use an item response theory (IRT)-based model to generate environmental risk scores (ERSs) and investigate the interactions between PRSs and midlife/later-life ERSs in LOAD across ethnoracial groups. We will utilize the All of Us genetic data and published GWAS summary statistics to construct LOAD PRSs and construct midlife and later-life ERSs based on various environmental indicators in the All of Us data, including social of determinants of health (e.g., education and incomes), physical conditions (e.g., hypertension, diabetes, and depression), and lifestyle (e.g., smoking, alcohol, and exercise) variables.

Anticipated Findings

Overall, we will examine one-by-one G×E interactions in LOAD, but also construct comprehensive scores, PRSs and midlife/later-life ERSs, for individuals across ethnoracial groups, collectively contributing to advancing our knowledge of G×E interactions on vulnerability and resilience to LOAD. Informed by data indicating an overall risk (PRS modified by ERS) for LOAD, this approach may enable clinicians and individuals to initiate disease screening and discuss life planning strategies. This study would also provide a crucial tool for informing the design and implementation of personalized therapeutic and preventative programs, enabling more precise and individualized approaches to the treatment and prevention of LOAD. This study not only enhances our understanding of progression of LOAD but also provides a foundation for developing targeted strategies to mitigate risk and bolster resilience, thereby advancing personalized interventions for diverse populations at different life stages.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

JIng Zhang - Research Fellow, University of Kentucky

Duplicate of Sleeping Disease & Alzheimer's Disease

Alzheimer's Disease is the most common form of dementia in the United States. This study means to identify phenotypic associations between AD & SD using machine learning (ML). The objective is to identify risk factors of mid-age and aging patients…

Scientific Questions Being Studied

Alzheimer's Disease is the most common form of dementia in the United States. This study means to identify phenotypic associations between AD & SD using machine learning (ML). The objective is to identify risk factors of mid-age and aging patients with SD to develop AD.

Project Purpose(s)

Disease Focused Research (Alzheimer's Disease)

Scientific Approaches

We will use patient data within the observation window to build prediction model. We plan to use ML models including logistic regression, decision tree, and the advanced tree-based model XGboost. Data will be split into training and testing sets. Model hyperparameters will be tuned using a cross validation strategy in the training set.

Anticipated Findings

Based on the proposed study design, we expect to identify the specific phenotypic associations between Sleep Disorders (SD) and Alzheimer's Disease (AD) through the use of Machine Learning (ML) techniques. This will involve identifying various risk factors associated with mid-aged and older patients with SD that may predispose them to developing AD. Our models should illuminate individual-level outcomes for patients with AD, drawing on a range of structured patient records including demographic details, symptom profiles, comorbid conditions, medications, and Social Determinants of Health (SDoH). The outcomes from this study would not only deepen our understanding of the association between SD and AD but also provide practical tools and insights to help mitigate the impact of Alzheimer's disease.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Daoming Lyu - Research Fellow, Cornell University

Subscribe to Email Updates