Health Disparities

Health Disparities

Traditionally, the practice of Medicine has been predicated on our identification of DNA differences between healthy people and patients. These differences have been guiding medical decisions. We have now generated strong evidence that “who we are” is just as important as any DNA differences.
 

Dimensions that can affect health outcomes

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We use the term “sex” to refer to the biological characteristics that define males and females. An individual’s sex affects or even defines the chance of having a disease. For example, pancreatic cancer is more common in males than females. >Explore
Sex
 
Race/Ethnicity refers to genetically differentiated individuals sharing a phenotype. We showed that race/ethnicity shapes the production of molecules that regulate protein levels. The mechanisms behind some diseases will thus differ in people from different races/ethnicities. >Explore
Race/Ethnicity
 
People of the same race/ethnicity living in the same geographic area form a population. We showed that population origin shapes the production of molecules that regulate protein levels. The mechanisms behind some diseases will thus differ in people from different populations. >Explore
Population Origin
Environment refers to all modifiable factors (food, air, water, quality of life, etc.) that shape an organism’s survival, development, and evolution. We showed that the environment can shape disease mechanisms by affecting the production of molecules that regulate protein levels. >Explore
Environment
 
Aging refers to organism-wide events that make an organism progressively unable to handle damage, disease, etc. We showed that age shapes the production of molecules that regulate protein levels. The mechanisms behind some diseases will thus differ in people with different ages. >Explore
Age

Improving our Understanding of Disease

Our findings highlight the important need for a paradigm shift: research into understanding what sets disease in motion and shapes its progression needs to take into account previously unknown molecules as well as variables that up to now had been considered inconsequential. We discovered that personal attributes such as sex, race/ethnicity, population origin and age determine one’s propensity for a given disease, how the disease will progress, how severe the disease will be, what therapeutic options are available, etc. Our research aims at improving our understanding of these dependencies.

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Health Disparities – Highlights

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miRNAs, tRFs and race in triple negative breast cancer. >Read
MINTbase v2. Among the new features, you can now browse TCGA data. >Read
miRNA isoforms in the breast cancer context. >Read
The Molecular Biology of Precision Medicine: Gender, Population and Race. >Read
IsomiRs in human LCLs depend on population and gender. >Read

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