Use Case: Use Open PIC-SURE to Investigate Asthma in Healthy and Obese Adult Populations
In this section, the functionalities of Open PIC-SURE will be described in the context of a scientific use case. Specifically, let’s say I am interested in investigating asthma in relation to obesity in adults.
I’m interested in two cohorts: obese adults with a body mass index (BMI) greater than 30 and healthy adults with a BMI between 18.5 and 24.9. However, I have not yet submitted a Data Access Request and therefore am not authorized to access any datasets.
First, let’s explore cohort A: Healthy adults with a BMI between 18.5 and 24.9 in Framingham Heart Study (FHS).
Search for 'age'.
Apply ‘FHS’ study tag to view only ‘age’ variables within the Framingham Heart Study (phs000007).
Select the variable of interest. You may notice many variables that appear similar. These variables may be located in different datasets, or tables, but contain similar information. Open up the variable information modal by clicking on the row containing the variable of interest to learn more.
Filter to adults only by clicking the filter icon next to the variable. I am interested in adults, so I will set the minimum age to 18, then click “Add filter to query”.
Now, let’s filter to healthy adults with a BMI between 18.5 and 24.9. Similar to before, we will search ‘BMI’. We can narrow down the search results using the variable-level tags by including terms related to our variable of interest (such as ‘continuous’ to view only continuous variables) and excluding out-of-scope terms (such as ‘allergy’). After selecting the variable of interest, we can filter to the desired ranges before adding the filter to our query. Notice how the total number of participants in our cohort changes.
Finally, we will filter for participants who have asthma. Search for the variable 'asthma' and select the B128 Asthma variable to select participants with a value "Yes".
Note the total participant count in the Data Summary.
We can easily modify our filters to explore cohort B: Obese adults with a body mass index (BMI) greater than 30 in Framingham Heart Study.
Edit the BMI filter by clicking the edit icon in the Added Variable Filters section. Change the range to have a minimum of 30 and no maximum.
Note the total participant count in the Data Summary.
We can easily repeat these steps for other studies, such as the Genetic Epidemiology of COPD (COPDGene) study, and create a table like the one below. By comparing these two studies, I can see that COPDGene may be more promising for my research since it contains many more participants in my cohorts of interest than FHS does.
| Study | N participants in cohort A: Healthy adults with a BMI between 18.5 and 24.9 | N participants in cohort B: Obese adults with a body mass index (BMI) greater than 30 |
|---|---|---|
Framingham Heart Study (FHS) | 38 +/- 3 | 57 +/- 3 |
Genetic Epidemiology of COPD (COPDGene) | 488 +/- 3 | 868 |
I can then use the "Request" button in the Participant Counts by Study Tool to go directly to the study's dbGaP page and begin submitting a Data Access Request (DAR).
Last updated
