As the UK Government sets out its 10-year vision for the NHS to become a predictive, preventative, and digitally enabled health system, there is a growing recognition that healthcare must evolve to meet the complexity of chronic disease.
The Fit for the Future: 10 Year Health Plan for England acknowledges this head-on, calling for a decisive shift in how we detect, diagnose and treat long-term conditions that currently consume more than 80% of the UK’s healthcare budget. This is no small task.
But advances in combinatorial analytics, genomics and clinical informatics are making it possible to approach chronic diseases not as monolithic challenges but as a series of more tractable solutions for interrelated biological factors.
Novel insights into the mechanisms that drive complex diseases are beginning to unlock new frontiers in precision medicine – offering hope not only for the NHS, but for health systems and patients around the world.
Beyond single genes
Unlike monogenic disorders – conditions caused by mutations in a single gene – most chronic diseases, such as cardiovascular disease, diabetes, Alzheimer’s and other dementias, obesity, respiratory diseases and women’s health conditions are driven by the complex interplay of multiple genes and environmental factors.
Traditional diagnosis of complex diseases often relies on the presentation of symptoms that can also occur in many different diseases, such as pelvic pain or fatigue. This can leave huge unmet medical need among patients whose diagnosis (and therefore treatment) is wrong and others for whom their disease may be missed for years. When this happens, diseases are finally treated only when they become more serious (and expensive), rather than years earlier when they can still be prevented.
Fortunately, new ‘precision’ or mechanism-based approaches, once only available for cancer, are now made possible for complex diseases. Combinatorial analytics allow us to identify specific constellations of genetic variants – common single nucleotide polymorphisms (SNPs) interacting and working in combination – that make up the key risk and resilience factors for complex diseases.
These provide an unprecedented view into the disease pathways affecting individual patients – revealing the real underlying cause of their disease, and predicting which symptoms they might experience, and which treatments are most likely to work for them.
For example, recent studies have shown that in complex diseases such as long Covid, Alzheimer’s, amyotrophic lateral sclerosis and endometriosis, patients can be stratified into distinct subgroups based on their underlying mechanisms. Such mechanistic insights are crucial not only for accurate diagnosis and differential triage, but also for selecting the most appropriate interventions for patients. They also help drug developers find genes and mechanisms that play a role in multiple diseases, including across several neurodegenerative conditions.
From genomic insight to clinical action
The real breakthrough comes when this knowledge is translated into practical clinical tools. Using low-cost, non-invasive sampling – such as saliva or buccal (cheek) swabs – and genotyping arrays (a common and widely available technology to analyse variations in DNA, used extensively during the Covid-19 pandemic), it is now possible to identify an individual’s disease-associated mechanisms to predict their lifetime risk of and resilience to major chronic diseases.
Unlike whole genome sequencing, which is expensive and difficult to scale equitably, these ‘Mechanostic’ tests are quick to run, inexpensive to produce and stable enough to be used at home or in primary care settings the world over.
Critically, these new tools do not rely on ‘black box’ approaches like most AI models or polygenic risk scores, which distil millions of genetic data points into a single number using complex, often unpublished algorithms, without clearly showing how that number was calculated or what it truly means for an individual’s health.
Instead, mechanostic tests are based on transparent, validated combinatorial models that are easier to interpret, reproduce better across different ancestries and are more amenable to regulatory oversight.
This enables them to be deployed as part of routine care – guiding referral decisions, informing pharmacological choices and offering early warnings of disease progression.
Mechanism-based medicine
By identifying patients’ disease mechanisms clinicians can intervene earlier, monitor more effectively and tailor treatments more precisely.
Mechanostic insights can also reveal actively protective biological pathways that can be leveraged to slow or prevent disease entirely, even in high-risk patients.
This is the promise of mechanism-based precision medicine and truly preventative healthcare. Where chronic conditions can be prevented before they become irreversible, and where patients receive treatments aligned with their biology, rather than trial-and-error symptom management.
Such an approach supports the sustainability and affordability of healthcare. By rapidly triaging patients with greater accuracy and identifying the right interventions for the right patients earlier, we can reduce unnecessary appointments, prevent costly misdiagnoses, avoid ineffective treatments, accelerate diagnosis and shorten time to therapeutic benefit. It’s a smarter allocation of resources – and it’s vital in a world of rising demand and constrained budgets.
Health equity through scalable tools
Health systems everywhere are facing the challenge of delivering more with less, while also addressing longstanding disparities in outcomes. Mechanism-based precision medicine offers a scalable way to help do both.
Rare genetic variants (SNPs or mutations) can be highly informative in monogenic diseases, but they are typically specific to certain families, small subpopulations or single ancestries. That means a risk model built on rare variants in one population often performs poorly when applied to others.
By contrast, common SNPs occur more frequently across all human populations. While no single SNP explains much risk on its own, in combination they form stable patterns of disease biology that are reproducible across ancestries. This makes mechanostic models based on common SNPs much more transferable and equitable – they are not limited to people of European descent, which has historically dominated genomic research, but can provide useful insights across more diverse groups.
This matters for health equity. If we want predictive, preventative tools to benefit everyone, we need models that are valid across different ancestries. Mechanostics provide that foundation. They make it possible to deliver risk and resilience insights at scale – in underserved communities and across global health systems.
A global opportunity
The UK is well positioned to lead this transformation. With world-class life sciences, a single-payer health system and deep genomics expertise, it has the tools to demonstrate how data-driven, personalised medicine can be integrated into mainstream clinical care.
But the opportunity is not limited to the NHS. Around the world, health systems are grappling with the same pressures – from the rising cost of chronic disease to the need for equitable access to high-quality care.
The key question is: ‘How do we deliver better healthcare?’
At PrecisionLife, we believe that mechanism-based precision medicine using Mechanostic tests, provides the vital bridge between aspiration and implementation of ambitious plans to make healthcare more predictive, preventative and personalised.
Understanding which mechanisms underpin a patient’s disease has transformed cancer care with a raft of effective precision and personally targeted drugs, but this has not been possible with more complex and costly chronic diseases, until now.
Using mechanostic tests, we can predict lifetime risk of disease, identify the cause, accurately triage patients and help to identify and develop optimal treatments that target the causes of disease and not just the symptoms.
And while these tools are ideally suited to help transform the UK’s NHS, the need is far greater. Our ambition is to help healthcare systems around the world to reduce the unsustainable burden of chronic disease.
As we move into this next phase of healthcare transformation, this shift from symptom-based to mechanism-based medicine may prove to be one of the most important innovations of our time – reshaping how we predict, treat and ultimately prevent conditions that impact billions of lives.
Author
Dr Steve Gardner PhD
Co-founder and CEO of PrecisionLife, chair of the UK BioIndustry Association’s Data, AI and Genomics Advisory Committee, member of the Scientific Advisory Board of Out Future Health, and expert in residence at Oxford University.
This article was originally published by our sister publication Healthcare Leader.
