George Busby, Allelica CSO & Co-Founder
The publication of the updated Global Burden of Disease (GBD) Reports is a timely reminder of the many different diseases that affect the global population. With somewhat morbid curiosity, it’s fascinating to read of the major drivers of disease and death across the globe, communicated with an incredible range of tables and visualizations.
It’s easy to get lost in the granularity of all this data. Take COVID-19 for example: a whopping 12% of all deaths globally in 2020/2021 were caused by the virus. However, this general figure masks significant heterogeneity at the country level: the pandemic actually led to lower than expected numbers of deaths in some countries, demonstrating both the complexity of the effect that the virus has had on population health, but also that disease burden is unevenly distributed across populations.
I find the GBD most useful for looking at the big trends across different countries and continents to get a better understanding of the macro forces shaping the health of the 8 billion people alive today. Packed full of numbers, the Institute of Health Metrics and Evaluation (IHME) provides metrics for disease that can help provide a handle on what’s really driving population health, or the lack of it.
COVID-19 aside, what we find is a familiar story to anyone watching global health trends: cardiovascular diseases remain the largest killer of people across the world and the biggest driver of disease. Intriguingly, diseases of the nervous system are now the leading overall cause of disease burden across the world. This includes diseases and conditions like stroke, migraine, and dementia.
Cancer also plays a key role in global disease trends, with breast, prostate, lung and colorectal being the main contributors to overall mortality. Importantly, a significant proportion of cancers, perhaps as many as 40%, are ultimately related to modifiable risk factors.
What these data show then, is that many of the main drivers of the huge burden of disease on population health can be reduced through prevention. We all stand to benefit with longer healthier lives from better risk prediction.
From population trends to individual prediction
It is possible for individuals like you or me to mitigate our underlying risk of disease. We can adapt our lifestyles to decrease our risk of stroke and Alzheimer’s Disease, take therapeutics like statins to drive down our ASCVD risk, and attend regular screening and imaging to identify cancers as early as possible.
So, while it will never be possible to remove the chances of getting these common chronic diseases, we can reduce our risk through better risk management.
The relationship between risk factors and disease
If we’re serious about driving down the burden of preventable diseases such as those reported in the GBD, we need to start with ways to identify risk and then manage those who have high risk.
Fortunately, there are a number of low hanging fruits when it comes to lowering risk. For example, tobacco use and obesity are key risk factors for many diseases, which is why national campaigns have focused on stopping people smoking and lowering weight. Reducing obesity and tobacco use have broad knock-on burden-reducing effects on a wide range of diseases.
However, these cases also highlight the complexities of apparently simple population health messaging. Despite the proven links between smoking and ASCVD and multiple cancers, and obesity and chronic diseases like diabetes, it has proved stubbornly difficult to control these risk factors. Indeed, there are now more people with overweight alive on the planet today than at any point before in human history.
Why is this? The globalization and industrialization of food production, represented by the ubiquity of ultra-processed food is affecting populations and their health across the globe. But a key reason is that despite understanding the ultimate role that being overweight plays in disease risk, we are not actually that good at implementing programs to mitigate risk. Obesity levels continue to stubbornly rise, despite all of us understanding the negative impacts that this condition has on health.
Obesity is a straightforward disease to diagnose. We can measure height and weight, compute Body Mass Index (BMI) and then understand whether this number is above or below a predefined threshold. And as we’ve discussed, it is because of its effect as a risk factor for many additional diseases that it is essential to continue to focus on driving down obesity.
But what about risk factors that are less easy to measure?
Assessing risk using outdated tools
There are of course other risk factors for common diseases. I now want to focus on how we can identify and use these to identify people at higher risk with the ultimate aim of working to drive down this risk through the initiation of campaigns aimed at reducing modifiable risk factors.
When it comes to population health, we already have a few tools at our disposal. There is a range of biomarkers that can be measured by physicians: BMI, LDL cholesterol, blood pressure, HB1Ac. And for cardiovascular disease and a few cancers, there are some risk models that take into account multiple factors and biomarkers that can be combined into an overall assessment of an individual’s risk.
However, the doctor’s standard toolbox does not contain the ability to measure every risk factor that has been associated with disease, including an individual’s genetic risk of common, chronic diseases as measured through polygenic risk score, or PRS.
PRSs measure an individual’s genetic risk of disease and — as we’ve written about before — they are increasingly being shown to be a key component of disease risk, which are currently not being utilized at anywhere near the level that their predictive ability warrants.
The effect of this is that people who are at high risk for common disease are consistently being undetected.
They are leaving doctor’s offices following routine checkups on the false assumption that they are fine, when really they are at high risk.
Building better tools
If we want to arm our primary care physicians with the best tools for them to identify patients at high risk so they can work to drive down the considerable burden of preventable disease, they should be using all of the data that they have at their disposal. And this needs to include an assessment of polygenic risk.
Patients and their physicians are increasingly benefiting from the opportunity to share their treatment decisions. PRSs can be another data point to bring to the table as prevention journeys are mapped out:
A patient with low 10-year clinical risk of ASCVD and a high PRS for coronary artery disease? Keep an eye on those modifiable risk factors and maintain a healthy lifestyle.
What about a patient at 8% 10-year clinical risk of ASCVD and a high PRS for coronary artery disease? Consider the PRS as a risk enhancer and consider cholesterol management as per guidelines.
For a woman in her 40s, use Allelica’s PRS-integrated clinical risk model to assess risk. If the combination of her clinical and genetic risk of breast cancer pushes her above a 20% lifetime risk threshold, then consider alternating her annual mammogram with a more accurate MRI.
Unmasking the invisible population at risk
In these and other use cases, Allelica’s PRS enabled tools can be used to provide the necessary additional data points, and Allelica is on hand, through our provider and patient education, advice on recommendations, and alignment with guidelines to help navigate care pathways after a PRS test has been completed.
The benefit?
With PRS tests, physicians can identify high risk individuals who are currently under the radar and invisible using current tools. And while physicians would never allow an individual with obesity to leave their office without a discussion of the implications of this condition and a plan on how to deal with it, physicians are routinely allowing at risk patients for a range of common diseases to leave their office, none the wiser.
The time has come to use PRSs to unmask these invisible individuals so that the preventable burden of common disease can be reduced.
