Personal well-being guides based on our genetic make-up that have the potential to predict disease risks are becoming increasingly possible

You may be familiar with a range of tips for living a healthy life: watch your weight, exercise, eat nutritious food and do not smoke, for example.

What if you could combine these lifestyle factors with a host of other variables to learn your risk of developing specific diseases, to help catch and treat them early or prevent them altogether?

Dr Victor Ortega, associate director of the Mayo Clinic's Centre for Individualised Medicine, in the US state of Arizona, explains how science is drawing ever closer to making such personal health forecasts possible.

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Previously inconceivable, such personal guides to well-being are becoming increasingly possible because of new and sophisticated technologies that capture data spanning entire genomes - complete sets of genetic material, or DNA - in our bodies, Ortega says.

Pulmonologist and genomic scientist Dr Victor Ortega is associate director of the Mayo Clinic's Centre for Individualised Medicine. Photo: Mayo Clinic

The complex scores are compiled from a combination of data from thousands to hundreds of thousands of a person's DNA sequence variants. This type of data has the potential to predict disease risks, such as heart disease, diabetes, asthma and specific cancers.

"Imagine knowing your genetic predisposition for having a heart attack in your 50s, or if you're in the top 5 per cent of the population for the risk of cancer or diabetes based on data from your whole genome," Ortega says. "With this knowledge, you could make informed lifestyle choices and receive enhanced screenings to mitigate that risk."

As a pulmonologist - someone who specialises in conditions that affect the respiratory system, including the airways and lungs - and genomic scientist, Ortega is leading a charge to breathe new life into precision medicine advancements.

His mission is rooted in a deep commitment to health equities that was inspired by his grandmother.

"My grandmother died of asthma, and that should not have happened. She was Puerto Rican like me, and Puerto Ricans have the highest severity and frequency of asthma of any ethnic group in the world," Ortega says.

"They also represent less than 1 per cent of people in genetic studies. So, I've made it a life mission to develop cures and diagnostics for people like my grandma, and for all people."

New and sophisticated technologies can capture data spanning entire genomes. Photo: Shutterstock

Each person has millions of genetic variants, each having a small effect. But together, these variants can increase the risk of getting a condition.

A polygenic risk score estimates the overall risk someone has of getting a disease by adding up the small effects of variants throughout an individual's entire genome.

Polygenic risk scores are not used to diagnose diseases. Some people who do not have a high-risk score for a certain disease can still be at risk of getting the disease or might already have it.

Other people with high-risk scores may never get the disease.

People with the same genetic risk can have different outcomes depending on other factors such as lifestyle that determine one's lifelong environmental exposures, also called the exposome.

It's going to take considerable work and planning, but it really is the way of the future

Dr Victor Ortega on providing polygenic risk scores for all

Ortega says that getting to the point where all people know their polygenic risk scores will require a solid foundation of "omics" research and data sets.

Omics is an emerging multidisciplinary field of biological sciences that encompasses genomics (genes), proteomics (proteins), epigenomics (how cells control gene activity without changing the DNA sequence), transcriptomics (the study of the "transcriptome", which includes all ribonucleic acid, or RNA, molecules within an organism), metabolomics (provides a comprehensive snapshot of the biochemistry of a biological system) and more.

It will also require cutting-edge technologies and further discoveries of gene-disease links. Ortega believes all of these are within his team's expertise and capabilities.

"It's going to take considerable work and planning, but it really is the way of the future," he says.

In the shorter term, Ortega plans to transition more omics discoveries from research laboratories to the clinic. Omics data can help identify the molecular culprits driving a person's disease, as well as biomarkers that can lead to the development of targeted treatments and diagnostics.

Recent omics discoveries at Mayo Clinic's Centre for Individualised Medicine have enabled scientists to predict antidepressant response in people with depression, and discover a potential therapeutic strategy for bone marrow cancer.

Ortega says science is drawing ever closer to making personal health forecasts possible, through analysis of all DNA variants in the body, combined with lifestyle factors. Photo: Shutterstock

Scientists have also used omics to pinpoint genetic variations that potentially increase the risk for severe Covid-19; uncover potential clues for preventing and treating gliomas; and unravel the genetic mystery of a rare neurodevelopmental disorder.

Drawing from his years of extensive clinical experience in treating patients with severe respiratory illnesses, Ortega is also working to expand genomic testing to a broader set of diseases. He highlights the centre's collaborative Programme for Rare and Undiagnosed Diseases as an effective model that he hopes to expand.

The programme conducts targeted genomic testing for patients with a suspected rare genetic disease.

Ortega says expanding this strategy to more diseases will help build collaborations across the Mayo Clinic organisation and educate more clinicians on genomics. It may also ensure the most effective genomic sequencing tests are given to patients, improving patient care and outcomes.

Ortega is also leading the development of a polygenic risk score framework, beginning with interstitial lung disease.

This condition, marked by progressive scarring of lung tissue, is influenced by both rare gene variants and a collection of more common variants, all of which are captured together in polygenic risk scores.

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