Bharanidharan Radha Saseendrakumar
Project Personnel, University of California, San Diego
20 active projects
Alcohol and Glaucoma
Scientific Questions Being Studied
Scientific research question: How does alcohol affect glaucoma prevalence, risk, and severity?
Why this is important: There has been contradicting evidence on how alcohol affects glaucoma. Research has shown acute alcohol consumption reduces intraocular pressure while the evidence for chronic consumption is unclear. Our study hopes to better elucidate how alcohol affects glaucoma, which deserves particular attention given the high prevalence of alcohol consumption and the danger of glaucoma.
Project Purpose(s)
- Disease Focused Research (Glaucoma)
Scientific Approaches
The datasets that we will be using are mainly: alcohol data, primary open-angle glaucoma data, and genetic data. Research methods: we will analyze any association between alcohol and glaucoma outcomes, detect whether there is a dose-response relationship to this association, if any, and whether genetics modulate the association. Guided statistics will be used to answer our research questions.
Anticipated Findings
Given the conflicting evidence regarding alcohol use on glaucoma, the anticipated findings are hard to predict. This highlights the importance of our project, as All of Us provides a large and diverse participant pool for us to better validate existing literature findings on alcohol and glaucoma.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierResearch Team
Owner:
- Jo-Hsuan Wu - Research Fellow, University of California, San Diego
- Leo Li Tang Meller - Graduate Trainee, University of California, San Diego
- Sally Baxter - Research Fellow, University of California, San Diego
- Kiana Tavakoli - Research Fellow, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
- Bonnie Huang - Graduate Trainee, Northwestern University
SDHA in Eye Conditions - v6 Dataset
Scientific Questions Being Studied
We are planning to explore disparities in healthcare access and utilization for patients with eye conditions across different demographic groups. We would like to evaluate risk of developing advanced/severe disease in different eye conditions, and understand how social determinants contribute to this risk while adjusting for other known risk factors. We are also interested in understanding the availability of social determinants of health data in this data repository compared to EHR clinical data warehouses alone.
Project Purpose(s)
- Population Health
Scientific Approaches
We will build cohorts of patients with various eye diseases (i.e. diabetic retinopathy, retinal vein occlusions, glaucoma, etc.). Then we will develop concept sets and extract data on outcomes (i.e. development of complications), as well as predictors including clinical data and social data. We will draw on survey data and EHR data within All of Us. When genomic data and wearable data become available, we are interested in evaluating those data sources as well. We will use statistical modeling and machine learning to generate predictive models.
Anticipated Findings
We anticipate that there may be differential risk for developing complications based on disparities in healthcare access and utilization for patients with eye conditions.
Demographic Categories of Interest
- Race / Ethnicity
- Age
- Geography
- Disability Status
- Access to Care
- Education Level
- Income Level
Data Set Used
Registered TierResearch Team
Owner:
- Varsha Varkhedi - Undergraduate Student, University of California, San Diego
- Tonya Lee - Graduate Trainee, University of California, San Diego
- Sophia Sidhu - Graduate Trainee, University of California, San Diego
- Sally Baxter - Research Fellow, University of California, San Diego
- Kaela Acuff - Graduate Trainee, University of California, San Diego
- Gordon Ye - Undergraduate Student, University of California, San Diego
- Catherine Sheils - Research Fellow, University of California, San Diego
- Bita Shahrvini - Graduate Trainee, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
- Arash Delavar - Graduate Trainee, University of California, San Diego
Collaborators:
- Joy Guo - Graduate Trainee, University of California, San Diego
- Bonnie Huang - Graduate Trainee, Northwestern University
- Kiana Tavakoli - Research Fellow, University of California, San Diego
Systemic Disease and Glaucoma
Scientific Questions Being Studied
We have previously published a predictive model of glaucoma progression using electronic health record (EHR) data pertaining to systemic attributes from a single institution. We aim to use the All of Us dataset to 1) serve as external validation for this single-center model and 2) to train new models focused on predicting glaucoma progression using systemic predictors. This is important to understand whether the original findings are generalizable and provide additional knowledge about the utility of systemic predictors on a national-level dataset.
Project Purpose(s)
- Disease Focused Research (Primary open angle glaucoma)
- Other Purpose (This work is the result of an All of Us Research Program Demonstration Project. Demonstration Projects are efforts by the All of Us Research Program designed to meet the goal of ensuring the quality and utility of the Research Hub as a resource for accelerating precision medicine. This work has been approved, reviewed, and overseen by the All of Us Research Program Science Committee and Data and Research Center to ensure compliance with program policy. )
Scientific Approaches
We plan to primarily work with EHR data contained in All of Us for a cohort of adult participants diagnosed with primary open-angle glaucoma. We will extract data on systemic conditions and medications for this cohort, as well as physical measurements and vital signs. We will clean the data such that the format is consistent with the data from our previously published model. Then, we will use this data as an external validation of a logistic regression model derived from our prior study that was based at a single academic center. Next, we will use All of Us data to train a new set of models, using techniques such as logistic regression, random forests, and artificial neural networks. We will optimize these models using feature selection methods and class balancing procedures. By evaluating performance metrics such as area under the curve (AUC), precision, recall, and accuracy, we will assess whether we can achieve superior predictive performance when training models using All of Us.
Anticipated Findings
We anticipate that the All of Us data will validate the findings from the model, which demonstrated that blood pressure-related metrics and certain medication classes had predictive value for glaucoma progression. In addition, we anticipate that the models trained with All of Us data will outperform the model trained with single institution data due to larger sample size and greater diversity. These findings will support further investigation in understanding the relationship between systemic conditions like blood pressure with glaucoma progression.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Tsung-Ting Kuo - Early Career Tenure-track Researcher, University of California, San Diego
- Sally Baxter - Research Fellow, University of California, San Diego
- Roxana Loperena Cortes - Other, All of Us Program Operational Use
- Paulina Paul - Project Personnel, University of California, San Diego
- Lucila Ohno-Machado
- Luca Bonomi - Project Personnel, Vanderbilt University Medical Center
- Jihoon Kim - Project Personnel, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
Collaborators:
- Sophia Sidhu - Graduate Trainee, University of California, San Diego
- Francis Ratsimbazafy - Other, All of Us Program Operational Use
- Nghia Nguyen - Research Fellow, University of California, San Diego
- Gordon Ye - Undergraduate Student, University of California, San Diego
- Bonnie Huang - Graduate Trainee, Northwestern University
- Arash Delavar - Graduate Trainee, University of California, San Diego
- Suad Alshammari - Graduate Trainee, Virginia Commonwealth University
- Silas Contaifer - Graduate Trainee, Virginia Commonwealth University
- Kerry Goetz - Project Personnel, National Institutes of Health (NIH)
- Joshua Morriss - Graduate Trainee, Virginia Commonwealth University
- VIRGINIA UNIVERSITY - Graduate Trainee, Virginia Commonwealth University
- Alireza Kamalipour - Research Fellow, University of California, San Diego
Metformin and Glaucoma v6
Scientific Questions Being Studied
There remain limited treatment options for glaucoma outside lowering intraocular pressure, which is not effective in all patients. Metformin, an antihyperglycemic drug used for the management of diabetes, is being increasingly explored for its neuroprotective effects in the eye. We would like to investigate the relationship between metformin use and primary open-angle glaucoma incidence and progression using All of Us Data, given the limited studies focusing on this topic.
Project Purpose(s)
- Drug Development
Scientific Approaches
We will build a cohort of patients with diabetes aged 40 and older, with no prior glaucoma diagnosis at baseline to assess glaucoma incidence. We will build a cohort of patients with diabetes aged 40 and older with an existing diagnosis of glaucoma to assess glaucoma progression. Then we will develop concept sets and extract data on outcomes (i.e. diagnosis of glaucoma, glaucoma progression represented by need for glaucoma surgery), as well as predictors including clinical data and social data. Analyses will be performed in R notebooks within the All of Us Workbench environment.
Anticipated Findings
We anticipate that metformin use will be associated with a reduced risk of developing primary open-angle glaucoma and reduced risk of progression to glaucoma surgery.
Demographic Categories of Interest
- Race / Ethnicity
- Age
- Geography
- Disability Status
- Access to Care
- Education Level
- Income Level
Data Set Used
Registered TierResearch Team
Owner:
- Jo-Hsuan Wu - Research Fellow, University of California, San Diego
- Sophia Sidhu - Graduate Trainee, University of California, San Diego
- Sally Baxter - Research Fellow, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
Collaborators:
- Bonnie Huang - Graduate Trainee, Northwestern University
Glaucoma Severity
Scientific Questions Being Studied
This study intends to analyze various socioeconomic characteristics across glaucoma stages to assess if disease severity correlates with factors such as race/ethnicity, income, education level, or insurance status. The same cohort will then be used to see if certain barriers to care identified in the Health Care Access and Utilization Survey correlate with worsening glaucoma severity. This will help guide interventions to improve eye care utilization and eye care disparities.
Project Purpose(s)
- Population Health
Scientific Approaches
Our cohort is made up of patients diagnosed with glaucoma and separated by stage to include mild, moderate, severe, and unspecified with limits to ensure patients are not counted multiple times. Patients with multiple glaucoma diagnoses are excluded. The desired characteristics such as age, age at first diagnosis, income, education, insurance status, and race/ethnicity have been added to the concept set as well as questions relating to eye care and barriers to care chosen from the HCAUS. Variables will be compared across glaucoma stages using chi square analysis and odds ratios. Findings will guide us towards potential interventions to improve healthcare disparities. Analysis will be conducted within the NIH All of Us Workspace.
Anticipated Findings
Anticipated findings include disparities based on socioeconomic factors leading to increased severity of disease among patients from disadvantaged backgrounds. It is anticipated that answers to the HCAUS will highlight barriers to care which may be contributing to these disparities and lead to the development of action plans which will help alleviate these disparities.
Demographic Categories of Interest
- Race / Ethnicity
- Age
- Geography
- Access to Care
- Education Level
Data Set Used
Registered TierResearch Team
Owner:
- Sally Baxter - Research Fellow, University of California, San Diego
- Kaela Acuff - Graduate Trainee, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
Metformin and Glaucoma
Scientific Questions Being Studied
There remain limited treatment options for glaucoma outside lowering intraocular pressure, which is not effective in all patients. Metformin, an antihyperglycemic drug used for the management of diabetes, is being increasingly explored for its neuroprotective effects in the eye. We would like to investigate the relationship between metformin use and primary open-angle glaucoma incidence and progression using All of Us Data, given the limited studies focusing on this topic.
Project Purpose(s)
- Drug Development
Scientific Approaches
We will build a cohort of patients with diabetes aged 40 and older, with no prior glaucoma diagnosis at baseline to assess glaucoma incidence. We will build a cohort of patients with diabetes aged 40 and older with an existing diagnosis of glaucoma to assess glaucoma progression. Then we will develop concept sets and extract data on outcomes (i.e. diagnosis of glaucoma, glaucoma progression represented by need for glaucoma surgery), as well as predictors including clinical data and social data. Analyses will be performed in R notebooks within the All of Us Workbench environment.
Anticipated Findings
We anticipate that metformin use will be associated with a reduced risk of developing primary open-angle glaucoma and reduced risk of progression to glaucoma surgery.
Demographic Categories of Interest
- Race / Ethnicity
- Age
- Geography
- Disability Status
- Access to Care
- Education Level
- Income Level
Data Set Used
Registered TierResearch Team
Owner:
- Jo-Hsuan Wu - Research Fellow, University of California, San Diego
- Sophia Sidhu - Graduate Trainee, University of California, San Diego
- Sally Baxter - Research Fellow, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
Collaborators:
- Kaela Acuff - Graduate Trainee, University of California, San Diego
Original - Social Determinants and Healthcare Access in Eye Conditions
Scientific Questions Being Studied
We are planning to explore disparities in healthcare access and utilization for patients with eye conditions across different demographic groups. We would like to evaluate risk of developing advanced/severe disease in different eye conditions, and understand how social determinants contribute to this risk while adjusting for other known risk factors. We are also interested in understanding the availability of social determinants of health data in this data repository compared to EHR clinical data warehouses alone.
Project Purpose(s)
- Population Health
Scientific Approaches
We will build cohorts of patients with various eye diseases (i.e. diabetic retinopathy, retinal vein occlusions, glaucoma, etc.). Then we will develop concept sets and extract data on outcomes (i.e. development of complications), as well as predictors including clinical data and social data. We will draw on survey data and EHR data within All of Us. When genomic data and wearable data become available, we are interested in evaluating those data sources as well. We will use statistical modeling and machine learning to generate predictive models.
Anticipated Findings
We anticipate that there may be differential risk for developing complications based on disparities in healthcare access and utilization for patients with eye conditions.
Demographic Categories of Interest
- Race / Ethnicity
- Age
- Geography
- Disability Status
- Access to Care
- Education Level
- Income Level
Data Set Used
Registered TierResearch Team
Owner:
- Terrence Lee - Graduate Trainee, University of California, San Diego
- Sally Baxter - Research Fellow, University of California, San Diego
- John McDermott - Graduate Trainee, University of California, San Diego
- Grace Ahn - Graduate Trainee, University of California, San Diego
- Gordon Ye - Undergraduate Student, University of California, San Diego
- Alison Chan - Graduate Trainee, University of California, San Diego
- Bita Shahrvini - Graduate Trainee, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
- Arash Delavar - Graduate Trainee, University of California, San Diego
Collaborators:
- Hua Ou - Mid-career Tenured Researcher, National Institutes of Health (NIH)
Social Determinants and Healthcare Access in Eye Conditions - v5 Dataset
Scientific Questions Being Studied
We are planning to explore disparities in healthcare access and utilization for patients with eye conditions across different demographic groups. We would like to evaluate risk of developing advanced/severe disease in different eye conditions, and understand how social determinants contribute to this risk while adjusting for other known risk factors. We are also interested in understanding the availability of social determinants of health data in this data repository compared to EHR clinical data warehouses alone.
Project Purpose(s)
- Population Health
Scientific Approaches
We will build cohorts of patients with various eye diseases (i.e. diabetic retinopathy, retinal vein occlusions, glaucoma, etc.). Then we will develop concept sets and extract data on outcomes (i.e. development of complications), as well as predictors including clinical data and social data. We will draw on survey data and EHR data within All of Us. When genomic data and wearable data become available, we are interested in evaluating those data sources as well. We will use statistical modeling and machine learning to generate predictive models.
Anticipated Findings
We anticipate that there may be differential risk for developing complications based on disparities in healthcare access and utilization for patients with eye conditions.
Demographic Categories of Interest
- Race / Ethnicity
- Age
- Geography
- Disability Status
- Access to Care
- Education Level
- Income Level
Data Set Used
Registered TierResearch Team
Owner:
- Terrence Lee - Graduate Trainee, University of California, San Diego
- Sally Baxter - Research Fellow, University of California, San Diego
- John McDermott - Graduate Trainee, University of California, San Diego
- Grace Ahn - Graduate Trainee, University of California, San Diego
- Gordon Ye - Undergraduate Student, University of California, San Diego
- Alison Chan - Graduate Trainee, University of California, San Diego
- Bita Shahrvini - Graduate Trainee, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
- Arash Delavar - Graduate Trainee, University of California, San Diego
Collaborators:
- Joy Guo - Graduate Trainee, University of California, San Diego
- Alireza Kamalipour - Research Fellow, University of California, San Diego
- Priyanka Soe - Project Personnel, University of California, San Diego
- Mahasweta Nayak - Undergraduate Student, University of California, San Diego
- Cecilia Vallejos - Undergraduate Student, University of California, San Diego
SDHA in Eye Conditions - v5 Dataset
Scientific Questions Being Studied
We are planning to explore disparities in healthcare access and utilization for patients with eye conditions across different demographic groups. We would like to evaluate risk of developing advanced/severe disease in different eye conditions, and understand how social determinants contribute to this risk while adjusting for other known risk factors. We are also interested in understanding the availability of social determinants of health data in this data repository compared to EHR clinical data warehouses alone.
Project Purpose(s)
- Population Health
Scientific Approaches
We will build cohorts of patients with various eye diseases (i.e. diabetic retinopathy, retinal vein occlusions, glaucoma, etc.). Then we will develop concept sets and extract data on outcomes (i.e. development of complications), as well as predictors including clinical data and social data. We will draw on survey data and EHR data within All of Us. When genomic data and wearable data become available, we are interested in evaluating those data sources as well. We will use statistical modeling and machine learning to generate predictive models.
Anticipated Findings
We anticipate that there may be differential risk for developing complications based on disparities in healthcare access and utilization for patients with eye conditions.
Demographic Categories of Interest
- Race / Ethnicity
- Age
- Geography
- Disability Status
- Access to Care
- Education Level
- Income Level
Data Set Used
Registered TierResearch Team
Owner:
- Terrence Lee - Graduate Trainee, University of California, San Diego
- Tonya Lee - Graduate Trainee, University of California, San Diego
- Sophia Sidhu - Graduate Trainee, University of California, San Diego
- Sally Baxter - Research Fellow, University of California, San Diego
- Kaela Acuff - Graduate Trainee, University of California, San Diego
- John McDermott - Graduate Trainee, University of California, San Diego
- Grace Ahn - Graduate Trainee, University of California, San Diego
- Gordon Ye - Undergraduate Student, University of California, San Diego
- Alison Chan - Graduate Trainee, University of California, San Diego
- Bita Shahrvini - Graduate Trainee, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
- Arash Delavar - Graduate Trainee, University of California, San Diego
Collaborators:
- Francis Ratsimbazafy - Other, All of Us Program Operational Use
- Jun Qian - Other, All of Us Program Operational Use
- Joy Guo - Graduate Trainee, University of California, San Diego
- Bonnie Huang - Graduate Trainee, Northwestern University
Depression Fitbit Study (Clinical Phenotyping)
Scientific Questions Being Studied
Major depressive disorder (MDD) is associated with changes in physical activity, fidgeting/restlessness, energy levels, and sleep patterns. It has also been established that most MDD patients have recurrent episodes, separated by periods of "remission" that may last 2 months or more. The purpose of this study is to assess the ability of the Fitbit data to longitudinally distinguish individuals with and without a depression diagnosis during a transitional phase coded in the medical record (i.e., a new or change in diagnostic code). We will focus primarily on daily physical activity (minutes of activity, steps traveled, elevation traveled), energy expenditure (calories burned), and heart rate.
Project Purpose(s)
- Disease Focused Research (major depressive disorder)
- Social / Behavioral
Scientific Approaches
We will build datasets and cohorts of individuals meeting the MDD/depression diagnostic criteria, as well as subsets for severity and additional modifiers. We will perform time series analysis of the Fitbit data with machine learning models, and assess their performance in the above questions. We will look specifically for trends and patterns in the time series Fitbit data that may be unique to the MDD/depression group.
Anticipated Findings
Consumer-grade technology has been pushing into the field of health and wellness. While previous studies have evaluated the performance of wearables in psychiatric and cardiovascular diseases, few have the sample sizes or data collection lengths made possible by the All of Us. We know that MDD comes with it changes in physical activity, and positive results may reinforce the role of wearable technology as a confirmatory step in the diagnostic process. Null results, on the other hand, will highlight the unique challenges of consumer health-related technologies, especially as these devices are becoming more and more common.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Sally Baxter - Research Fellow, University of California, San Diego
- Gordon Ye - Undergraduate Student, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
Collaborators:
- Tonya Lee - Graduate Trainee, University of California, San Diego
Depression Fitbit Study (PHQ-9 Phenotyping)
Scientific Questions Being Studied
Depression is associated with changes in physical activity, fidgeting/restlessness, energy levels, and sleep patterns. It has also been established that most depression patients have recurrent episodes, separated by periods of "remission" that may last 2 months or more. The purpose of this study is to assess the ability of longitudinal Fitbit data to identify individuals with worsening symptoms of depression, and those potentially entering a depressive episode using longitudinal PHQ-9 survey responses, a standard and validated depression screening tool. We will focus primarily on daily physical activity (minutes of activity, steps traveled, elevation traveled), energy expenditure (calories burned), and heart rate.
Project Purpose(s)
- Disease Focused Research (major depressive disorder)
- Social / Behavioral
- Educational
Scientific Approaches
We will extract Fitbit data from participants who have responded to the PHQ-9 questions administered as part of the COPE survey. We will first compute PHQ-9 scores to stratify individuals by depression severity (or healthy control status). We will then train and evaluate machine learning models using the Fitbit data prior to PHQ-9 survey completion to predict the onset of worsening depressive symptoms and potential depressive episodes.
Anticipated Findings
Consumer-grade technology including smartwatches has been pushing into the field of health and wellness. While previous studies have evaluated the utility of mobile health (mHealth) technologies, few have the sample sizes or data collection lengths made possible by the All of Us. This is particularly the case for mHealth research in psychiatric diseases: the vast majority focus on data collected from small cohorts that may not be representative of real-world diversity. We know that depression comes with it changes in physical activity, and positive results may reinforce the role of wearable technology as a confirmatory or early detection step in the diagnostic process. Null results, on the other hand, will highlight the unique challenges of consumer health-related technologies despite their adoption by the public.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierResearch Team
Owner:
- Gordon Ye - Undergraduate Student, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
Collaborators:
- Prothit Halder - Undergraduate Student, Scripps Research
- Nolan Chai - Undergraduate Student, University of California, San Diego
- Natalie Kwong - Undergraduate Student, University of California, San Diego
Systemic Disease and Glaucoma (Cloned)
Scientific Questions Being Studied
We have previously published a predictive model of glaucoma progression using electronic health record (EHR) data pertaining to systemic attributes from a single institution. We aim to use the All of Us dataset to 1) serve as external validation for this single-center model and 2) to train new models focused on predicting glaucoma progression using systemic predictors. This is important to understand whether the original findings are generalizable and provide additional knowledge about the utility of systemic predictors on a national-level dataset.
Project Purpose(s)
- Disease Focused Research (Primary open angle glaucoma)
- Other Purpose (This work is the result of an All of Us Research Program Demonstration Project. Demonstration Projects are efforts by the All of Us Research Program designed to meet the goal of ensuring the quality and utility of the Research Hub as a resource for accelerating precision medicine. This work has been approved, reviewed, and overseen by the All of Us Research Program Science Committee and Data and Research Center to ensure compliance with program policy. )
Scientific Approaches
We plan to primarily work with EHR data contained in All of Us for a cohort of adult participants diagnosed with primary open-angle glaucoma. We will extract data on systemic conditions and medications for this cohort, as well as physical measurements and vital signs. We will clean the data such that the format is consistent with the data from our previously published model. Then, we will use this data as an external validation of a logistic regression model derived from our prior study that was based at a single academic center. Next, we will use All of Us data to train a new set of models, using techniques such as logistic regression, random forests, and artificial neural networks. We will optimize these models using feature selection methods and class balancing procedures. By evaluating performance metrics such as area under the curve (AUC), precision, recall, and accuracy, we will assess whether we can achieve superior predictive performance when training models using All of Us.
Anticipated Findings
We anticipate that the All of Us data will validate the findings from the model, which demonstrated that blood pressure-related metrics and certain medication classes had predictive value for glaucoma progression. In addition, we anticipate that the models trained with All of Us data will outperform the model trained with single institution data due to larger sample size and greater diversity. These findings will support further investigation in understanding the relationship between systemic conditions like blood pressure with glaucoma progression.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Tsung-Ting Kuo - Early Career Tenure-track Researcher, University of California, San Diego
- Sally Baxter - Research Fellow, University of California, San Diego
- Roxana Loperena Cortes - Other, All of Us Program Operational Use
- Francis Ratsimbazafy - Other, All of Us Program Operational Use
- Paulina Paul - Project Personnel, University of California, San Diego
- Melissa Patrick - Project Personnel, All of Us Program Operational Use
- Lucila Ohno-Machado
- Luca Bonomi - Project Personnel, Vanderbilt University Medical Center
- Kelsey Mayo - Other, All of Us Program Operational Use
- Jihoon Kim - Project Personnel, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
- Ashley Able - Other, All of Us Program Operational Use
Collaborators:
- Chenjie Zeng - Research Fellow, National Human Genome Research Institute (NIH-NHGRI)
Evaluating the Role of Potential Neuroprotective Agents in Glaucoma
Scientific Questions Being Studied
Glaucoma, a chronic neurodegenerative disease of retinal ganglion cells (RGCs), is a leading cause of irreversible blindness worldwide. Its management currently focuses on lowering intraocular pressure to slow disease progression. However, disease-modifying, neuroprotective treatments for glaucoma remain a major unmet need. Several studies have been performed demonstrating potential for "repurposing" existing medications for protecting RGCs and mitigating the risk of developing glaucoma and/or slowing the progression of glaucoma. However, many of these studies have been performed in basic science settings using animal models and lack large-scale human data for validation. In this workspace, we plan to explore existing data on a diverse nationwide cohort to further evaluate the potential validity of candidate neuroprotective medications for influencing risk of glaucoma.
Project Purpose(s)
- Disease Focused Research (Glaucoma)
Scientific Approaches
We will primarily employ methods in clinical epidemiology, biostatistics, and machine learning. For questions relating to risk of developing glaucoma, we will examine data from a general older adult population and investigate whether use of candidate medications decreases the risk of developing glaucoma. For questions relating to risk of progressing glaucoma, we will examine data from a cohort of participants with existing diagnoses of glaucoma and evaluate whether use of candidate medications increases the risk of diagnosis codes related to greater severity. Depending on cohort size and the specific medication of interest, we may use cohort study or case-control study approaches. To examine associations of risk, we may use regression modeling, chi-squared analyses, longitudinal modeling, or machine learning methods, depending on the available cohort sizes and the characteristics of the predictor and outcome variables.
Anticipated Findings
We anticipate that this work will provide some validation of findings from the basic science literature by using real-world clinical data to support findings in animal models. Some of the medications may prove to be supported by human data, whereas others may not. Furthermore, we may even identify novel candidate medications not previously identified in the literature. This will advance the scientific knowledge of novel therapeutics for the prevention and treatment of glaucoma.
Demographic Categories of Interest
- Age
Data Set Used
Registered TierResearch Team
Owner:
- Sally Baxter - Research Fellow, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
Research Program for Vision Surveillance: Diabetes and Diabetic Retinopathy
Scientific Questions Being Studied
How do data from the All of Us database compare against known data sources that are considered to be representative of the general population and have been traditionally used in vision health surveillance activities (such as NHANES, NHIS, etc.)? How does All of Us compare to existing big-data sources such as IQVIA?
There is increasing interest in understanding how social factors impact health and vision outcomes. Social determinants of health are important considerations for disease management and prognosis, and our representative use case (diabetes and diabetic retinopathy) has huge implications for our health system as the leading cause of blindness and visual impairment among working-age adults in the United States. By answering the above questions, we can determine whether the All of Us database is representative and may be broadly generalizable for future studies.
Project Purpose(s)
- Control Set
Scientific Approaches
- Develop standard cohort definition for diabetes
- Develop standard cohort definition for diabetic retinopathy
- Determine prevalence of diabetes and compare across different data sources – All of Us, NHANES, NHIS, IQVIA
o Numerator: Number of adults with diabetes
o Denominator: Total number of adults available in data source
- Determine prevalence of diabetic retinopathy and compare across different data sources – All of Us, NHANES, NHIS, IQVIA
o Numerator: Number of adults with diabetic retinopathy
o Denominator: Total number of adults available in data source vs. total number of adults with diabetes
- For prevalence calculations, will need to establish defined study periods and ensure consistency across data sources
- Potential analyses:
o Look at state/regional variations
o Examine demographics (age, gender, race, ethnicity) of cohorts across data sources
- Identify areas of similarity/alignment vs. differences
Anticipated Findings
If we are able to demonstrate that the All of Us database is representative and aligns with existing nationwide data sources, then findings regarding links between social determinants and vision health outcomes using All of Us would be felt to be more broadly generalizable. On the other hand, if there are major discrepancies between All of Us and previously established data sources, this would be important information for the vision research community to be aware of, and this could even inform future efforts to make the database more representative.
Demographic Categories of Interest
- Race / Ethnicity
- Age
- Geography
- Access to Care
Data Set Used
Registered TierResearch Team
Owner:
- Sally Baxter - Research Fellow, University of California, San Diego
- Priyanka Soe - Project Personnel, University of California, San Diego
- Michael Wolfe - Graduate Trainee, University of California, San Diego
- Cecilia Vallejos - Undergraduate Student, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
SDHA in Eye Conditions - v4 Dataset
Scientific Questions Being Studied
We are planning to explore disparities in healthcare access and utilization for patients with eye conditions across different demographic groups. We would like to evaluate risk of developing advanced/severe disease in different eye conditions, and understand how social determinants contribute to this risk while adjusting for other known risk factors. We are also interested in understanding the availability of social determinants of health data in this data repository compared to EHR clinical data warehouses alone.
Project Purpose(s)
- Population Health
Scientific Approaches
We will build cohorts of patients with various eye diseases (i.e. diabetic retinopathy, retinal vein occlusions, glaucoma, etc.). Then we will develop concept sets and extract data on outcomes (i.e. development of complications), as well as predictors including clinical data and social data. We will draw on survey data and EHR data within All of Us. When genomic data and wearable data become available, we are interested in evaluating those data sources as well. We will use statistical modeling and machine learning to generate predictive models.
Anticipated Findings
We anticipate that there may be differential risk for developing complications based on disparities in healthcare access and utilization for patients with eye conditions.
Demographic Categories of Interest
- Race / Ethnicity
- Age
- Geography
- Disability Status
- Access to Care
- Education Level
- Income Level
Data Set Used
Registered TierResearch Team
Owner:
- Terrence Lee - Graduate Trainee, University of California, San Diego
- Sally Baxter - Research Fellow, University of California, San Diego
- John McDermott - Graduate Trainee, University of California, San Diego
- Grace Ahn - Graduate Trainee, University of California, San Diego
- Gordon Ye - Undergraduate Student, University of California, San Diego
- Alison Chan - Graduate Trainee, University of California, San Diego
- Bita Shahrvini - Graduate Trainee, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
- Arash Delavar - Graduate Trainee, University of California, San Diego
Social Determinants and Healthcare Access in Eye Conditions - v4 Dataset
Scientific Questions Being Studied
We are planning to explore disparities in healthcare access and utilization for patients with eye conditions across different demographic groups. We would like to evaluate risk of developing advanced/severe disease in different eye conditions, and understand how social determinants contribute to this risk while adjusting for other known risk factors. We are also interested in understanding the availability of social determinants of health data in this data repository compared to EHR clinical data warehouses alone.
Project Purpose(s)
- Population Health
Scientific Approaches
We will build cohorts of patients with various eye diseases (i.e. diabetic retinopathy, retinal vein occlusions, glaucoma, etc.). Then we will develop concept sets and extract data on outcomes (i.e. development of complications), as well as predictors including clinical data and social data. We will draw on survey data and EHR data within All of Us. When genomic data and wearable data become available, we are interested in evaluating those data sources as well. We will use statistical modeling and machine learning to generate predictive models.
Anticipated Findings
We anticipate that there may be differential risk for developing complications based on disparities in healthcare access and utilization for patients with eye conditions.
Demographic Categories of Interest
- Race / Ethnicity
- Age
- Geography
- Disability Status
- Access to Care
- Education Level
- Income Level
Data Set Used
Registered TierResearch Team
Owner:
- Terrence Lee - Graduate Trainee, University of California, San Diego
- Sally Baxter - Research Fellow, University of California, San Diego
- John McDermott - Graduate Trainee, University of California, San Diego
- Grace Ahn - Graduate Trainee, University of California, San Diego
- Gordon Ye - Undergraduate Student, University of California, San Diego
- Alison Chan - Graduate Trainee, University of California, San Diego
- Bita Shahrvini - Graduate Trainee, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
- Arash Delavar - Graduate Trainee, University of California, San Diego
Collaborators:
- Priyanka Soe - Project Personnel, University of California, San Diego
- Mahasweta Nayak - Undergraduate Student, University of California, San Diego
- Cecilia Vallejos - Undergraduate Student, University of California, San Diego
Health disparities in periocular malignancies
Scientific Questions Being Studied
The goal of this study is to identify social and medical factors associated with periocular malignancies. Identifying and addressing health disparities can minimize the progression to severe outcomes, such as loss of eye, which have a detrimental effect on patients’ quality of life.
Project Purpose(s)
- Population Health
- Control Set
Scientific Approaches
We plan to run a case-control study using electronic health records and sociodemographic data from All of Us dataset. We will use R or Python to run our analysis and look for trends.
Anticipated Findings
Identifying and addressing health disparities can minimize the progression to severe outcomes, such as periocular malignancy.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Sally Baxter - Research Fellow, University of California, San Diego
- Niloofar Radgoudarzi - Graduate Trainee, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
Collaborators:
- Joy Guo - Graduate Trainee, University of California, San Diego
Retinal Vein Occlusion and associated risk factors
Scientific Questions Being Studied
To assess for risk factors for retinal vein occlusion (RVO) among participants in the NIH All of Us database, particularly social risk factors that have not been well-studied, including substance use.
Project Purpose(s)
- Disease Focused Research (retinal vein occlusion)
Scientific Approaches
Data will be extracted regarding demographics, co-morbidities, income, housing, insurance, and substance use. Opioid use will be defined by relevant diagnosis and prescription codes, with prescription use >30 days. Controls will be sampled at a 4:1 control to case ratio from a pool of individuals >18 years of age without a diagnosis of RVO and proportionally matched to the demographic distribution of the 2019 U.S. census. We will use multivariable logistic regression to identify medical and social determinants significantly associated with RVO. Statistical significance will be defined as p<0.05.
Anticipated Findings
Understanding RVO risk factors is important for primary prevention and improving visual outcomes. Several studies have demonstrated an increasing prevalence of RVO with age, but little consensus has been reached regarding associations with race and/or ethnicity. Other studies exploring medical risk factors have shown strong associations with hypertension, hyperlipidemia, diabetes mellitus, glaucoma and cigarette smoking. However, the majority of these studies were conducted on small populations limited to individuals identifying as Asian or white. Few studies have investigated associations with substance use outside of cigarettes and alcohol. The opioid epidemic began in the early 2000s, and as of 2019, more than 1.6 million Americans suffer from opioid use disorder. Given that long term opioid use increases risk of cardiovascular events such as myocardial infarction, an investigation into whether opioid use increases risk of retinal vascular disease, such as RVO, is warranted
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Sally Baxter - Research Fellow, University of California, San Diego
- John McDermott - Graduate Trainee, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
Duplicate (latest ver, for testing) of Systemic Disease and Glaucoma
Scientific Questions Being Studied
We have previously published a predictive model of glaucoma progression using electronic health record (EHR) data pertaining to systemic attributes from a single institution. We aim to use the All of Us dataset to 1) serve as external validation for this single-center model and 2) to train new models focused on predicting glaucoma progression using systemic predictors. This is important to understand whether the original findings are generalizable and provide additional knowledge about the utility of systemic predictors on a national-level dataset.
Project Purpose(s)
- Disease Focused Research (Primary open angle glaucoma)
- Other Purpose (This work is the result of an All of Us Research Program Demonstration Project. Demonstration Projects are efforts by the All of Us Research Program designed to meet the goal of ensuring the quality and utility of the Research Hub as a resource for accelerating precision medicine. This work has been approved, reviewed, and overseen by the All of Us Research Program Science Committee and Data and Research Center to ensure compliance with program policy. )
Scientific Approaches
We plan to primarily work with EHR data contained in All of Us for a cohort of adult participants diagnosed with primary open-angle glaucoma. We will extract data on systemic conditions and medications for this cohort, as well as physical measurements and vital signs. We will clean the data such that the format is consistent with the data from our previously published model. Then, we will use this data as an external validation of a logistic regression model derived from our prior study that was based at a single academic center. Next, we will use All of Us data to train a new set of models, using techniques such as logistic regression, random forests, and artificial neural networks. We will optimize these models using feature selection methods and class balancing procedures. By evaluating performance metrics such as area under the curve (AUC), precision, recall, and accuracy, we will assess whether we can achieve superior predictive performance when training models using All of Us.
Anticipated Findings
We anticipate that the All of Us data will validate the findings from the model, which demonstrated that blood pressure-related metrics and certain medication classes had predictive value for glaucoma progression. In addition, we anticipate that the models trained with All of Us data will outperform the model trained with single institution data due to larger sample size and greater diversity. These findings will support further investigation in understanding the relationship between systemic conditions like blood pressure with glaucoma progression.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
Old Duplicate of Systemic Disease and Glaucoma
Scientific Questions Being Studied
We have previously published a predictive model of glaucoma progression using electronic health record (EHR) data pertaining to systemic attributes from a single institution. We aim to use the All of Us dataset to 1) serve as external validation for this single-center model and 2) to train new models focused on predicting glaucoma progression using systemic predictors. This is important to understand whether the original findings are generalizable and provide additional knowledge about the utility of systemic predictors on a national-level dataset.
Project Purpose(s)
- Disease Focused Research (primary open angle glaucoma)
- Other Purpose (This work is the result of an All of Us Research Program Demonstration Project. Demonstration Projects are efforts by the All of Us Research Program designed to meet the goal of ensuring the quality and utility of the Research Hub as a resource for accelerating precision medicine. This work has been approved, reviewed, and overseen by the All of Us Research Program Science Committee and Data and Research Center to ensure compliance with program policy. )
Scientific Approaches
We will develop predictive models using the All of Us dataset using multivariable logistic regression, random forests, and artificial neural networks.
Anticipated Findings
We anticipate that the All of Us data will validate the findings from the model, which demonstrated that blood pressure-related metrics and certain medication classes had predictive value for glaucoma progression. In addition, we anticipate that the models trained with All of Us data will outperform the model trained with single institution data due to larger sample size and greater diversity.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Tsung-Ting Kuo - Early Career Tenure-track Researcher, University of California, San Diego
- Sally Baxter - Research Fellow, University of California, San Diego
- Roxana Loperena Cortes - Other, All of Us Program Operational Use
- Paulina Paul - Project Personnel, University of California, San Diego
- Lucila Ohno-Machado
- Luca Bonomi - Project Personnel, Vanderbilt University Medical Center
- Katherine Kim - Early Career Tenure-track Researcher, University of California, Davis
- Jihoon Kim - Project Personnel, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
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