Genomic Data – Not Race – May Enhance Chronic Disease Management

April 16, 2021 - Genomic data can provide more targeted insights into chronic disease management than those generated from traditional ethnic or racial labels like Hispanic or Black, a study published in Cell revealed.

Researchers noted that the complex patterns of genetic ancestry, uncovered from genomic data, could help providers understand which populations are more susceptible to diseases such as cancer, asthma, diabetes, and cardiovascular disease.

The data could also allow providers to develop early, targeted interventions.

“This is the first time researchers have shown how genetic ancestry data could be used to enhance our understanding of disease risk and management at a health system level," said senior author Eimear Kenny, PhD, Professor of Medicine, and Genetics and Genomic Sciences, at the Icahn School of Medicine at Mount Sinai.

"By linking this data directly to health outcomes, we believe we're contributing to an ongoing conversation to move beyond the current role of race and ethnicity in medicine."

Researchers used data from Mount Sinai’s BioMe BioBank program, one of the world’s leading repositories for genomic information for diverse populations. Leveraging machine learning tools, the team identified 17 distinct ethnic communities from among the 300,000 participants in the BioMe BioBank.

Researchers then linked this data to thousands of health outcomes contained in Mount Sinai’s EHRs. The findings showed that 25 percent of BioMe participants had genetic links to populations, including Ashkenazi Jewish and Puerto Rican, that made them more susceptible to certain genetic diseases.

"The traditional use of demographic data by health systems fails to capture the rich ethnic heritage of patients, and thus all the genetic and environmental factors that can affect rates of disease even within the same population," said Kenny, who is Founding Director of the Institute for Genomic Health at Mount Sinai.

"Our study used genomic data embedded in health system records to show how patients with origins from different countries in the Americas can have different rates of disease. For example, people of Puerto Rican and Mexican descent are broadly classified as Hispanic or Latinx, yet the former population has one of the highest rates of asthma in the world, while the latter population has one of the lowest."

According to researchers, the APOL1 gene – which can present a significantly higher risk of kidney and cardiovascular disease in patients who have it – is another reason for going beyond traditional demographic labels used by health systems.

The risk variants of APOL1 are most frequently seen in populations across the Americas that share African genetic ancestry, researchers noted.

However, the team also pointed out that there are many populations around the world of African descent that may not self-identify as African. So, these individuals may not know that they harbor those risk variants.

That knowledge gap may lead to these populations being underrepresented in APOL1 research, further contributing to health disparities.

“We demonstrate that embedding genomic data in health systems, and using it to infer genetic ancestry, will allow the development of evidence-based means to utilize race and ethnicity, genetic ancestry, and the socioeconomic determinants of health for both rare and common diseases,” the team wrote.

Researchers also stated that the use of advanced analytics models will play an important role in genomic data analysis.

“The network-based machine learning approaches applied here are highly scalable to very large datasets of individuals. As more genomic data become available in health systems globally and via large research projects, we anticipate approaches such as ours will uncover increasing nuances in the population structure,” the group said.

The study reveals the potential for genomics and genetic testing to revolutionize chronic disease management.

"Our study underscores that there are limits to the narrow demographic labels used in medicine and research today--and society in general, for that matter--to attempt to characterize disease and its risk factors," Kenny concluded.

"The types of information that can be derived from using biological markers of ancestry, however, convey a much richer and more sophisticated layer of understanding of disease risk and burden, one that could have enormous implications for health care systems globally."