Laura Goetz

Early Career Tenure-track Researcher, Translational Genomics Research Institute

3 active projects

Chemoprevention in CRC and Breast Ca

Previous studies have shown that the drugs metformin, statins and aspirin all may have chemoprotective effects on cancer. However, other studies have shown that the effects are mitigated when other variables and accounted for. The research in animal models have…

Scientific Questions Being Studied

Previous studies have shown that the drugs metformin, statins and aspirin all may have chemoprotective effects on cancer. However, other studies have shown that the effects are mitigated when other variables and accounted for. The research in animal models have shown that there is a mechanistic reasoning behind the drug eliciting chemoprotective effects. Some studies have suggested that statins and metformin make work together to assert their effect. To date most of the studies have been in predominant population with European ancestry. Our primary goal is to determine if there is a chemoprotective effect seen with metformin, statins and aspirin. The secondary goal is to determine how these drugs in combination may change this relationship. These studies have predominant focused on colorectal and breast cancer.

Project Purpose(s)

  • Population Health

Scientific Approaches

For this study we will first determine if in the dataset we find any correlation between the drugs and prevention of colorectal or breast cancer. Then we plan to create a model to control for patient factors that may influence the relationship between the medication and cancer. Finally, we will create a model to determine how the medication may influence each other’s effect. To do this we will need to look at all patients that have electronic medical records data available. We will make a control set of adults that are 50 years old or older at the current time and have not taken metformin, statin or aspirin. We will then identify patients 50years old or older that have taken individually or in combination: metformin, statins and aspirin.

Anticipated Findings

We hope to find a positive correlation between the use of the medication and decreased cancer risk. This would contribute to the current literature because this study would be done on the most diverse population. This would add to the growing body of literature that these drugs may have chemoprotective effects.

Demographic Categories of Interest

  • Age

Research Team

Owner:

  • Laura Goetz - Early Career Tenure-track Researcher, Translational Genomics Research Institute
  • Chelsea Isom - Other, University of California, San Diego

Collaborators:

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

Duplicate of How to Get Started with Registered Tier Data

We recommend that all researchers explore the notebooks in this workspace to learn the basics of All of Us Program Data. What should you expect? This notebook will give you an overview of what data is available in the current…

Scientific Questions Being Studied

We recommend that all researchers explore the notebooks in this workspace to learn the basics of All of Us Program Data.

What should you expect? This notebook will give you an overview of what data is available in the current Curated Data Repository (CDR). It will also teach you how to retrieve information about Electronic Health Record (EHR), Physical Measurements (PM), and Survey data.

Project Purpose(s)

  • Educational
  • Methods Development
  • Other Purpose (This is an All of Us Tutorial Workspace. It is meant to provide instruction for key Researcher Workbench components and All of Us data representation.)

Scientific Approaches

This Tutorial Workspace contains two Jupyter Notebooks (one written in Python, the other in R). Each notebook is divided into the following sections:

1. Setup: How to set up this notebook, install and import software packages, and select the correct version of the CDR.
2. Data Availability Part 1: How to summarize the number of unique participants with major data types: Physical Measurements, Survey, and EHR;
3. Data Availability Part 2: How to delve a little deeper into data availability within each major data type;
4. Data Organization: An explanation of how data is organized according to our common data model.
5. Example Queries: How to directly query the CDR, using two examples of SQL queries to extract demographic data.
6. Expert Tip: How to access the base version of the CDR, for users that want to do their own cleaning.

Anticipated Findings

By reading and running the notebooks in this Tutorial Workspace, you will understand the following:

All of Us data are made available in a Curated Data Repository. Participants may contribute any combination of survey, physical measurement, and electronic health record data. Not all participants contribute all possible data types. Each unique piece of health information is given a unique identifier called a concept_id and organized into specific tables according to our common data model. You can use these concept_ids to query the CDR and pull data on specific health information relevant to your analysis. See our support article Learning the Basics of the All of Us Dataset for more info.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

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

Healthy Aging

The overall goal of our research is to incorporate polygenic variants with longitudinally measured routine blood chemistries and phenotypic measures into prediction models for healthy aging and longevity. We have used the UK Biobank data to create polygenic longevity scores(PLS)…

Scientific Questions Being Studied

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

Project Purpose(s)

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

Scientific Approaches

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

Anticipated Findings

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

Demographic Categories of Interest

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

Research Team

Owner:

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