Research Projects Directory

Duplicate of Phenotype - Type 2 Diabetes (v7)

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

Scientific Questions Being Studied

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

Project Purpose(s)

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

Scientific Approaches

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

Anticipated Findings

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

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Elina Guralnik - Graduate Trainee, George Mason University

Duplicate of Phenotype - Breast Cancer (v7)

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research.

Scientific Questions Being Studied

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research.

Project Purpose(s)

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

Scientific Approaches

Not Applicable

Anticipated Findings

By reading and running the Notebooks in this Phenotype Library Workspace, researchers can implement the following phenotype algorithms:

Ning Shang, George Hripcsak, Chunhua Weng, Wendy K. Chung, & Katherine Crew. Breast Cancer. Retrieved from https://phekb.org/phenotype/breast-cancer.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Elina Guralnik - Graduate Trainee, George Mason University

Duplicate of Phenotype - Type 2 Diabetes (v7)

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research.

Scientific Questions Being Studied

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research.

Project Purpose(s)

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

Scientific Approaches

Not Applicable

Anticipated Findings

By reading and running the Notebooks in this Phenotype Library Workspace, researchers can implement the following phenotype algorithms:

Jennifer Pacheco and Will Thompson. Northwestern University. Type 2 Diabetes Mellitus. PheKB; 2012 Available from: https://phekb.org/phenotype/18

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Elina Guralnik - Graduate Trainee, George Mason University

Duplicate of Phenotype - Dementia (v7)

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research.

Scientific Questions Being Studied

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research.

Project Purpose(s)

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

Scientific Approaches

Not Applicable

Anticipated Findings

By reading and running the Notebooks in this Phenotype Library Workspace, researchers can implement the following phenotype algorithms:

Ritchie, M., Denny, J., Crawford, D., Ramirez, A., Weiner, J., … Roden, D. (2010). Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. American Journal of Human Genetics. 87(2):310 doi: 10.1016/j.ajhg.2010.03.003

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Ellen Thompson - Graduate Trainee, Baylor College of Medicine

Duplicate of Phenotype - Dementia (v7)

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research. I am duplicating this notebook for introductory purposes.

Scientific Questions Being Studied

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research. I am duplicating this notebook for introductory purposes.

Project Purpose(s)

Educational
Other Purpose (This is an All of Us Phenotype Library Workspace created by the Researcher Workbench Support team. It is meant to demonstrate the implementation of key phenotype algorithms within the All of Us Research Program cohort.)

Scientific Approaches

Not Applicable

Anticipated Findings

By reading and running the Notebooks in this Phenotype Library Workspace, researchers can implement the following phenotype algorithms:

Ritchie, M., Denny, J., Crawford, D., Ramirez, A., Weiner, J., … Roden, D. (2010). Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. American Journal of Human Genetics. 87(2):310 doi: 10.1016/j.ajhg.2010.03.003

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Christina Magyar - Graduate Trainee, Baylor College of Medicine

Duplicate of Phenotype - Dementia (v7)-R

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research.

Scientific Questions Being Studied

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research.

Project Purpose(s)

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

Scientific Approaches

Not Applicable

Anticipated Findings

By reading and running the Notebooks in this Phenotype Library Workspace, researchers can implement the following phenotype algorithms:

Ritchie, M., Denny, J., Crawford, D., Ramirez, A., Weiner, J., … Roden, D. (2010). Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. American Journal of Human Genetics. 87(2):310 doi: 10.1016/j.ajhg.2010.03.003

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Gabrielle Nobles - Graduate Trainee, Baylor College of Medicine

Duplicate of Phenotype - Dementia (v7)-python

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research.

Scientific Questions Being Studied

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research.

Project Purpose(s)

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

Scientific Approaches

Not Applicable

Anticipated Findings

By reading and running the Notebooks in this Phenotype Library Workspace, researchers can implement the following phenotype algorithms:

Ritchie, M., Denny, J., Crawford, D., Ramirez, A., Weiner, J., … Roden, D. (2010). Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. American Journal of Human Genetics. 87(2):310 doi: 10.1016/j.ajhg.2010.03.003

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Gabrielle Nobles - Graduate Trainee, Baylor College of Medicine

Duplicate of BMI EWAS

Obesity has been found to accompany a multitude of molecular and metabolic perturbations including impaired cell signaling, insulin resistance, hyperlipidemia, and hypertension.

Scientific Questions Being Studied

Obesity has been found to accompany a multitude of molecular and metabolic perturbations including impaired cell signaling, insulin resistance, hyperlipidemia, and hypertension.

Project Purpose(s)

Educational

Scientific Approaches

Regression Models to identify significant CpGs
Pathway analysis for the potential biological links
Tools mainly being R

Anticipated Findings

Given the distinct genetic and environmental background of the Bangladeshi population, which may influence BMI-related epigenetic markers differently compared to Western cohorts, our research could uncover novel insights into the epigenetic mechanisms that modulate BMI effects.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Zheng Liao - Graduate Trainee, University of Chicago

BMI EWAS

Obesity has been found to accompany a multitude of molecular and metabolic perturbations including impaired cell signaling, insulin resistance, hyperlipidemia, and hypertension.

Scientific Questions Being Studied

Obesity has been found to accompany a multitude of molecular and metabolic perturbations including impaired cell signaling, insulin resistance, hyperlipidemia, and hypertension.

Project Purpose(s)

Educational

Scientific Approaches

Regression Models to identify significant CpGs
Pathway analysis for the potential biological links
Tools mainly being R

Anticipated Findings

Given the distinct genetic and environmental background of the Bangladeshi population, which may influence BMI-related epigenetic markers differently compared to Western cohorts, our research could uncover novel insights into the epigenetic mechanisms that modulate BMI effects.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Zheng Liao - Graduate Trainee, University of Chicago

WGS-QC

Our specific scientific questions is to understand the genetic determinants of glaucoma and their implications for disease risk prediction and management. We aim to identify genetic variants associated with glaucoma susceptibility through genome-wide association studies using whole-genome sequencing (WGS) data.…

Scientific Questions Being Studied

Our specific scientific questions is to understand the genetic determinants of glaucoma and their implications for disease risk prediction and management.
We aim to identify genetic variants associated with glaucoma susceptibility through genome-wide association studies using whole-genome sequencing (WGS) data. The importance of these questions lies in their potential to advance our understanding of glaucoma genetics, improve risk prediction, and inform personalized approaches to disease management.

Project Purpose(s)

Disease Focused Research (glaucoma)
Methods Development
Ancestry

Scientific Approaches

We will use whole genome sequencing data.
We will firstly run sample-level and variant-level QC on WGS data by using Hail and bcftools.
After QC, Regenie will be used to run GWAS to explore genotype-phenotype associations in glaucoma by correlating identified genetic variants with clinical phenotypes. Furthermore, we plan to develop polygenic risk scores (PRS) for glaucoma using PLINK.

Anticipated Findings

We anticipate identifying population-specific genetic risk factors and novel genetic variants for glaucoma within diverse populations represented in the All of Us cohort. Moreover, we plan to figure out the factors that are strongly correlated with high risk of genetic burdens. These findings can shed light on the biological pathways involved in glaucoma development and provide potential targets for therapeutic intervention. This knowledge can inform culturally sensitive interventions and address disparities in glaucoma care across diverse populations.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Yuyang Luo - Project Personnel, Mass General Brigham
Yan Zhao - Other, Mass General Brigham
Nazlee Zebardast - Early Career Tenure-track Researcher, Mass General Brigham
Elizabeth Rossin - Early Career Tenure-track Researcher, Broad Institute

Duplicate of Beginner Intro to AoU Data and the Workbench (v7)

This workspace contains multiple notebooks that assess users' understanding of the workbench and OMOP. These notebooks are meant to help users check their knowledge not only on Python, R, and SQL, but also on the general data structure and data…

Scientific Questions Being Studied

This workspace contains multiple notebooks that assess users' understanding of the workbench and OMOP. These notebooks are meant to help users check their knowledge not only on Python, R, and SQL, but also on the general data structure and data model used by the All of Us program.

Project Purpose(s)

Educational

Scientific Approaches

There are no scientific approach used in this workspace because it is meant for educational purposes only. We will cover all aspects of OMOP, and hence will use most datasets available in the workbench.

Anticipated Findings

We do not anticipate to have any findings. Instead, we are educating people on the use of the workbench and the common data model OMOP used by the program.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Yesol Sapozhnikov - Graduate Trainee, University of Idaho

FL Controlled Tier v7

Many genetic variants have been linked to complex diseases such as diabetes and obesity through genome-wide association studies (GWAS). GWAS has identified numerous genes associated with these conditions, and the use of polygenic risk scores (PRS), which aggregate these variants,…

Scientific Questions Being Studied

Many genetic variants have been linked to complex diseases such as diabetes and obesity through genome-wide association studies (GWAS). GWAS has identified numerous genes associated with these conditions, and the use of polygenic risk scores (PRS), which aggregate these variants, has improved disease prediction. However, most GWAS and PRS have been based on populations of European ancestry, potentially worsening health disparities. Therefore, it is essential to diversify GWAS and PRS research to include a broader range of ancestries. This project aims to create multi-ancestry GWAS for complex diseases such as diabetes, obesity, and their related complications, identifying potential therapeutic targets. Additionally, we seek to develop new PRS to enhance disease prediction across various populations.

Project Purpose(s)

Disease Focused Research (Type 2 diabetes, obesity, and complex human diseases)

Scientific Approaches

We will analyze and compile clinical and genetic data on complex human diseases with the following objectives: Data Curation: We plan to gather, standardize, and integrate data from large-scale, multi-ancestry cohorts, particularly focusing on diabetes and its associated genomic traits. This data will serve as the foundation for generating GWAS and developing PRS across diverse ancestries. Methodological Advancements: We will conduct GWAS of complex diseases in diverse ancestral groups to identify potential therapeutic targets. Additionally, we aim to develop new PRS that utilize Bayesian techniques, enhancing predictions by integrating summary statistics from related traits across various ancestries. Development and Evaluation: We will test PRS for their predictive performance. Furthermore, we plan to create risk prediction tools that combine clinical and genetic data, improving disease prediction capabilities.

Anticipated Findings

This project aims to identify potential therapeutic target genes through GWAS and enhance preventive strategies for complex diseases such as diabetes, obesity, and their complications by developing new PRS. Our goal is to contribute to drug development by identifying therapeutic targets, advancing precision medicine by improving the predictive power of PRS across diverse populations, and promoting health equity across all groups.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Satoshi Yoshiji - Research Fellow, Broad Institute

Duplicate of Beginner Intro to AoU Data and the Workbench (v7)

This workspace contains multiple notebooks that assess users' understanding of the workbench and OMOP. These notebooks are meant to help users check their knowledge not only on Python, R, and SQL, but also on the general data structure and data…

Scientific Questions Being Studied

This workspace contains multiple notebooks that assess users' understanding of the workbench and OMOP. These notebooks are meant to help users check their knowledge not only on Python, R, and SQL, but also on the general data structure and data model used by the All of Us program.

Project Purpose(s)

Educational

Scientific Approaches

There are no scientific approach used in this workspace because it is meant for educational purposes only. We will cover all aspects of OMOP, and hence will use most datasets available in the workbench.

Anticipated Findings

We do not anticipate to have any findings. Instead, we are educating people on the use of the workbench and the common data model OMOP used by the program.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Quanje Maxwell - Undergraduate Student, California State University, Fullerton

All of us workshop

This workspace contains multiple notebooks that assess users' understanding of the workbench and OMOP. These notebooks are meant to help users check their knowledge not only on Python, R, and SQL, but also on the general data structure and data…

Scientific Questions Being Studied

This workspace contains multiple notebooks that assess users' understanding of the workbench and OMOP. These notebooks are meant to help users check their knowledge not only on Python, R, and SQL, but also on the general data structure and data model used by the All of Us program.

Project Purpose(s)

Educational

Scientific Approaches

There are no scientific approach used in this workspace because it is meant for educational purposes only. We will cover all aspects of OMOP, and hence will use most datasets available in the workbench.

Anticipated Findings

We do not anticipate to have any findings. Instead, we are educating people on the use of the workbench and the common data model OMOP used by the program.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Hori Nguyen - Undergraduate Student, California State University, Fullerton

Duplicate of Beginner Intro to AoU Data and the Workbench (v7)

This workspace contains multiple notebooks that assess users' understanding of the workbench and OMOP. These notebooks are meant to help users check their knowledge not only on Python, R, and SQL, but also on the general data structure and data…

Scientific Questions Being Studied

This workspace contains multiple notebooks that assess users' understanding of the workbench and OMOP. These notebooks are meant to help users check their knowledge not only on Python, R, and SQL, but also on the general data structure and data model used by the All of Us program.

Project Purpose(s)

Educational

Scientific Approaches

There are no scientific approach used in this workspace because it is meant for educational purposes only. We will cover all aspects of OMOP, and hence will use most datasets available in the workbench.

Anticipated Findings

We do not anticipate to have any findings. Instead, we are educating people on the use of the workbench and the common data model OMOP used by the program.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Hamzah Deejay - Undergraduate Student, California State University, Fullerton

Duplicate of CV Disease Module

Train Users in using the All Of Us database

Scientific Questions Being Studied

Train Users in using the All Of Us database

Project Purpose(s)

Educational

Scientific Approaches

To explore Fitbit data and CV disease for training purposes.

Anticipated Findings

Training users in using the All Of Us database

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Aeisha Thomas - Other, Crown College

Collaborators:

Feseha Abebe-Akele - Early Career Tenure-track Researcher, Elizabeth City State University

Summer Institute dummy practice set

We are using this workspace to set up curricular activities to help community college instructors who are attending the Summer Institute to learn how to conduct research on the Researcher Workbench so that they can help their students to learn…

Scientific Questions Being Studied

We are using this workspace to set up curricular activities to help community college instructors who are attending the Summer Institute to learn how to conduct research on the Researcher Workbench so that they can help their students to learn these skills.

Project Purpose(s)

Educational

Scientific Approaches

Descriptive statistics on basic survey data and electronic health record data. We will not be using genomic data.

Anticipated Findings

Community college instructors will be able to successfully ask and answer questions that can be used to teach students about database research in the All of Us Researcher Workbench.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Rebecca Peterson - Research Associate, University of Utah

Duplicate of Beginner Intro to AoU Data and the Workbench (v7)

This workspace contains multiple notebooks that assess users' understanding of the workbench and OMOP. These notebooks are meant to help users check their knowledge not only on Python, R, and SQL, but also on the general data structure and data…

Scientific Questions Being Studied

This workspace contains multiple notebooks that assess users' understanding of the workbench and OMOP. These notebooks are meant to help users check their knowledge not only on Python, R, and SQL, but also on the general data structure and data model used by the All of Us program.

Project Purpose(s)

Educational

Scientific Approaches

There are no scientific approach used in this workspace because it is meant for educational purposes only. We will cover all aspects of OMOP, and hence will use most datasets available in the workbench.

Anticipated Findings

We do not anticipate to have any findings. Instead, we are educating people on the use of the workbench and the common data model OMOP used by the program.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Yee Chee Yong - Graduate Trainee, California State University, Fullerton
Sam Stewart - FW - Other, All of Us Program Operational Use
Izabelle Humes - Other, All of Us Program Operational Use
Chris Lord - Project Personnel, All of Us Program Operational Use

missingness_demographics

We are investigating the impact of common inclusion criteria for observational studies on different demographics.

Scientific Questions Being Studied

We are investigating the impact of common inclusion criteria for observational studies on different demographics.

Project Purpose(s)

Control Set

Scientific Approaches

We plan to use common techniques for defining a cohort for an observational study and then investigating the impact on sample size of demographic groups.

Anticipated Findings

We anticipate that these common inclusion criteria for observational research studies using electronic health records or claims data will decrease the sample size more for traditionally underrepresented demographic groups.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Yasaman Fatapour - Graduate Trainee, University of California, Irvine
Undina Gisladottir - Graduate Trainee, Columbia University
hongyu liu - Senior Researcher, Cedars-Sinai Medical Center

Cardiovascular disease risk factors in schizophrenia patients

Metabolic syndrome is a known risk factor for cardiovascular disease, which has been identified as one of the major contributors to the 10-25 year decreased life expectancy in schizophrenic patients. Compared to the general population, the prevalence of metabolic syndrome…

Scientific Questions Being Studied

Metabolic syndrome is a known risk factor for cardiovascular disease, which has been identified as one of the major contributors to the 10-25 year decreased life expectancy in schizophrenic patients. Compared to the general population, the prevalence of metabolic syndrome is five times greater in people with schizophrenia. Scientific questions that we would like to study is as follows: 1) Is prevalence of metabolic syndrome higher in patients with schizophrenia compared to general participants in All of Us dataset? 2) Is metabolic syndrome associated with a higher prevalence of hypertension, hyperlipidemia, and T2DM in schizophrenia patients? 3) Do schizophrenia patients with metabolic syndrome report worse qualify of life or perceive their health status to be worse than schizophrenia patients without the diagnosis of metabolic syndrome? 4) What are the risk factors of having metabolic syndrome in schizophrenia patients?

Project Purpose(s)

Disease Focused Research (Schizophrenia)

Scientific Approaches

In this study, we will include participants from All of Us dataset version 6. Schizophrenia cohort is defined as having at least one ICD-9/10 diagnosis code for schizophrenia. Metabolic syndrome cohort will be defined as having any 3 or more of the following conditions: 1) waist circumference more than 40 inches in men and 35 inches in women, 2) elevated triglycerides 150 milligrams per deciliter of blood (mg/dL), 3) reduced high-density lipoprotein cholesterol (HDL) less than 40 mg/dL in men or less than 50 mg/dL in women, 4) elevated fasting glucose of l00 mg/dL or greater, 5) blood pressure values of systolic 130 mmHg or higher and/or diastolic 85 mmHg or higher. Analysis will be performed in the web-based platform Jupyter Notebook using programming language R.

Anticipated Findings

We expect to have higher prevalence of metabolic syndrome in patients with schizophrenia compared to general participants in All of Us dataset. Metabolic syndrome is anticipated to be associated with higher prevalence of hypertension, hyperlipidemia, and T2DM in schizophrenia patients. Schizophrenia patients with metabolic syndrome is expected to report worse quality of life, mental or physical well-being. Smoking, excessive drinking, family history of diabetes, use of clozapine or olanzapine are expected to be associated with increased risk of metabolic syndrome in schizophrenia patients. These have been reported in various studies but data from larger national sample would contribute to the growing evidence the anticipated findings stated above.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Yong Eun - Research Associate, New York City Health & Hospitals

Metabolic syndrome in people with schizophrenia

Metabolic syndrome is a known risk factor for cardiovascular disease, which has been identified as one of the major contributors to the 10-25 year decreased life expectancy in schizophrenic patients. Compared to the general population, the prevalence of metabolic syndrome…

Scientific Questions Being Studied

Metabolic syndrome is a known risk factor for cardiovascular disease, which has been identified as one of the major contributors to the 10-25 year decreased life expectancy in schizophrenic patients. Compared to the general population, the prevalence of metabolic syndrome is five times greater in people with schizophrenia.
Scientific questions that we would like to study is as follows:
1) Is prevalence of metabolic syndrome higher in patients with schizophrenia compared to general participants in All of Us dataset?
2) Is metabolic syndrome associated with a higher prevalence of hypertension, hyperlipidemia, and T2DM in schizophrenia patients?
3) Do schizophrenia patients with metabolic syndrome report worse qualify of life or perceive their health status to be worse than schizophrenia patients without the diagnosis of metabolic syndrome?
4) What are the risk factors of having metabolic syndrome in schizophrenia patients?

Project Purpose(s)

Disease Focused Research (schizophrenia)

Scientific Approaches

In this study, we will include participants from All of Us dataset version 6. Schizophrenia cohort is defined as having at least one ICD-9/10 diagnosis code for schizophrenia. Metabolic syndrome cohort will be defined as having any 3 or more of the following conditions: 1) waist circumference more than 40 inches in men and 35 inches in women, 2) elevated triglycerides 150 milligrams per deciliter of blood (mg/dL), 3) reduced high-density lipoprotein cholesterol (HDL) less than 40 mg/dL in men or less than 50 mg/dL in women, 4) elevated fasting glucose of l00 mg/dL or greater, 5) blood pressure values of systolic 130 mmHg or higher and/or diastolic 85 mmHg or higher. Analysis will be performed in the web-based platform Jupyter Notebook using programming language R.

Anticipated Findings

We expect to have higher prevalence of metabolic syndrome in patients with schizophrenia compared to general participants in All of Us dataset. Metabolic syndrome is anticipated to be associated with higher prevalence of hypertension, hyperlipidemia, and T2DM in schizophrenia patients. Schizophrenia patients with metabolic syndrome is expected to report worse quality of life, mental or physical well-being. Smoking, excessive drinking, family history of diabetes, use of clozapine or olanzapine are expected to be associated with increased risk of metabolic syndrome in schizophrenia patients. These have been reported in various studies but data from larger national sample would contribute to the growing evidence the anticipated findings stated above.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Yong Eun - Research Associate, New York City Health & Hospitals

Collaborators:

Farbod Raiszadeh - Senior Researcher, New York City Health & Hospitals
Rahman Olusoji - Research Fellow, New York City Health & Hospitals
Ha Rim Kwak - Research Associate, New York City Health & Hospitals

Neuropsychiatric Disorders in Sex Chromosome Aneuploidies V7

Sex chromosome aneuploidies (SCAs), such as Klinefelter Syndrome and Turner Syndrome, are common genetic conditions. Studies of SCAs among pediatric cohorts have shown that these conditions can increase the risk of neurodevelopmental and psychiatric disorders (NPDs). However, the impact of…

Scientific Questions Being Studied

Sex chromosome aneuploidies (SCAs), such as Klinefelter Syndrome and Turner Syndrome, are common genetic conditions. Studies of SCAs among pediatric cohorts have shown that these conditions can increase the risk of neurodevelopmental and psychiatric disorders (NPDs). However, the impact of SCAs on the mental health of adults remains poorly characterized due to widespread underdiagnosis in the general population. Additionally, similar to other neurodevelopmental disorders, research suggests a correlation between SCAs and susceptibility to socioeconomic deprivation (PMID: 37578764). Yet, the relationship between SCAs and social determinants of health within the US population is not well described. The primary question of this project is “Are SCA associated with an increased risk of neuropsychiatric disorders (NPD)?” The secondary question of this project is “What is the relationship between SCAs and social determinants of health in a large US cohort?”

Project Purpose(s)

Disease Focused Research (Sex chromosome aneuploidies)
Ancestry

Scientific Approaches

We will screen for sex-chromosome aneuploidies using genotype array data. The detection SCAs will consist of identifying outliers in the number of X or Y chromosomes, based on the median intensity values of each sex chromosome from the array. We will use whole-genome data to orthogonally confirm the presence of a sex-chromosome aneuploidy called from the genotype array data. We plan to use comprehensive EHR data, conditions, drug exposures, lab/measurements, procedures, and survey data to create NPD outcomes and exposure variables. We will use logistic and linear regression to compare outcome variables (e.g. the prevalence of NPD, social determinants of health) in those with a sex chromosome aneuploidy compared to those without a sex chromosome aneuploidy.

Anticipated Findings

We will derive population-based estimates for NPD risk and social determinants of health associated with sex chromosome aneuploidy. The neurodevelopmental psychiatric, social determinants of health, and other medical outcomes of undiagnosed adults have not been integrated into medical descriptions of SCAs, which are heavily biased by their reliance on clinically ascertained pediatric cohorts. Uncovering an association between SCA and NPD, and 'medicalizing’ mental health conditions through disclosure of genetic test results may significantly decrease shame and stigma, increase self-advocacy, and lead to closer engagement with healthcare providers. Socioeconomic deprivation could significantly influence the morbidity of disorders associated with SCAs. Our research on the social determinants of health and SCAs could lead to improvements in both the physical and psychological health of affected individuals.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Alexander Berry - Research Associate, Geisinger Clinic

Collaborators:

Alex Winters - Research Associate, Geisinger Clinic

Lung Cancer Demonstration

• What are the germline variants, molecular markers, and reproductive factors associated with lung cancer in adult females within the All of Us cohort? • Do reproductive factors interact with molecular and genetic markers to increase lung cancer risk in…

Scientific Questions Being Studied

• What are the germline variants, molecular markers, and reproductive factors associated with lung cancer in adult females within the All of Us cohort?
• Do reproductive factors interact with molecular and genetic markers to increase lung cancer risk in adult females?

Project Purpose(s)

Educational

Scientific Approaches

a) Study design: nested case-control design
b) Study population:
• Case group: Adult female (≥18 years) diagnosed with lung cancer
• Control group: Adult female (≥18 years) without a history of lung cancer, matched on age, smoking status, and ethnicity.
c) Primary outcome: Incidence of lung cancer in female
d) Primary exposure:
• Reproductive factors: Age at menarche, Age at menopause, Age at first child, history of Oral Contraceptive pill, history of uterus/ovary removal, Pattern of menstruation, parity, History of Hormone replacement therapy, History of HPV vaccination
• Molecular factors: SNPs related to Estrogen Receptor (ER), Aromatase, PD-1/PDL1, Foxp3 expressing CD4+ cells/ Treg Cell, EGFR and Errb-2 gene
e) Covariates:
• Demographic:
Education, BMI, Residence, Occupation
• Clinical:
Histopathology, Site, Differentiation, Stage, Metastatic site

Anticipated Findings

Our research aims to enhance understanding of lung cancer susceptibility by exploring interactions between genetic and reproductive factors, informing targeted prevention and personalized treatments. By identifying specific SNPs associated with lung cancer and reproductive factors, we improve risk prediction models, enabling early interventions to reduce mortality rates. Integrating genetic findings with clinical data elucidates biological mechanisms underlying lung cancer susceptibility, potentially leading to novel therapies. Insights from our study have significant public health implications, informing tailored policies emphasizing modifiable lifestyle factors for lung cancer prevention, particularly among genetically vulnerable adult females.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

muhammad Rafiqul islam - Graduate Trainee, University of Chicago
Andrew Craver - Project Personnel, University of Chicago

Version 6 Exploring the All of Us data for hypertension and other data types

We are also replicating work of a previous study on hypertension prevalence across geographic states by Chandler et al. 2021 that requires us to use v6 of the data.

Scientific Questions Being Studied

We are also replicating work of a previous study on hypertension prevalence across geographic
states by Chandler et al. 2021 that requires us to use v6 of the data.

Project Purpose(s)

Educational

Scientific Approaches

We are also replicating work of a previous study on hypertension prevalence across geographic
states by Chandler et al. 2021 that requires us to use v6 of the data.

Anticipated Findings

We are also replicating work of a previous study on hypertension prevalence across geographic
states by Chandler et al. 2021 that requires us to use v6 of the data.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Justin Moore - Graduate Trainee, Baylor College of Medicine

HAP823

This study is for educational purposes to better understand major depression in African Americans. This study is for education purposes for a course.

Scientific Questions Being Studied

This study is for educational purposes to better understand major depression in African Americans. This study is for education purposes for a course.

Project Purpose(s)

Educational

Scientific Approaches

To analyze the African American race and major depression. we will analyze data and clean it to understand the probabilities.

Anticipated Findings

To understand the effectiveness of antidepressants

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Shema Salim - Graduate Trainee, George Mason University

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