Liver disease remains one of the few major disease areas where outcomes have not improved in line with other fields such as cardiovascular disease and cancer. In the latest episode of The Genetics Podcast, host and Sano Co-Founder and CEO Patrick Short spoke with Dr. Tim Jobson, Medical Director of Predictive Health Intelligence and Consultant Gastroenterologist at Somerset NHS Foundation Trust, about why that is and what it will take to change it.
The conversation focused on metabolic liver disease, late diagnosis, the limits of current care pathways, and the opportunity to use routine clinical data, genetics, and non-invasive testing to identify risk earlier. It also explores the LiveWell study, a collaboration between Predictive Health Intelligence, Somerset NHS Foundation Trust, Tawazun Health, and Sano Genetics, as an example of how this model can work in practice.
One of the clearest messages from the discussion is that liver disease is still too often detected only once it has become advanced. As Tim explains, many liver conditions progress silently for years. Patients may have subtle abnormalities in routine blood tests, but these signals are easy to miss when viewed one result at a time across busy clinical workflows.
That delay matters. By the time symptoms appear, some patients already have cirrhosis, liver cancer, or other complications that are much harder to treat. Tim describes late diagnosis as a defining challenge in hepatology, with many patients only learning they have liver disease when meaningful early intervention has already been missed.
This is especially important in metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as fatty liver disease. MASLD is increasingly common and closely linked to broader metabolic dysfunction, including obesity, diabetes, hypertension, cardiovascular disease, and some cancers. In that sense, liver disease is not an isolated problem. It is part of a wider population health challenge.
A core theme of the episode is that health systems already hold much of the data needed to spot risk earlier. The problem is not always lack of data. It is how to use it.
Tim discusses the Somerset Liver Improvement Programme and the development of hepatoSIGHT®, a case-finding approach that analyzes historic blood test data to identify patients whose results suggest emerging liver disease risk. Rather than relying on individual clinicians to pick up subtle patterns across thousands of test results, the system brings those signals together and looks for trajectories that suggest a patient needs review.
That approach is powerful because it works with routine data already generated in normal care. It does not depend on building an entirely new pathway from scratch. It starts by making better use of what the NHS already has.
As Tim puts it, the real goal is not broad "screening for liver disease" as a single condition. It is risk stratification. Liver disease includes multiple underlying diagnoses, and the challenge is to identify which people are most likely to need further assessment.
The episode also explores the role of genetics in liver disease, including where it is already part of routine care and where it may become more useful in the future.
Tim points to hereditary haemochromatosis as the clearest established example. Genetic testing is already used to confirm diagnosis and support family screening. Alpha-1 antitrypsin deficiency (AATD) is another area where genetics is clinically relevant, though it is still underused and its broader implications for coexisting liver disease are still being worked into practice.
For MASLD and related conditions, the picture is more emerging. There are clear genetic signals associated with disease risk and progression, but most of this remains in the research domain for now. Ultimately, genetics could help answer an important clinical question that current care still struggles with: which patients are most likely to progress, and therefore need closer follow-up or earlier intervention.
That matters because the stage of fibrosis is only one part of the story. Clinicians can increasingly assess how much liver scarring is already present, but they still have fewer tools to predict trajectory. Genetics may help fill part of that gap when used alongside routine blood tests and imaging.
This is where the LiveWell study becomes especially important.
LiveWell builds on the hepatoSIGHT® approach by combining longitudinal blood test data with at-home genetic testing and non-invasive FibroScan assessments. Participants were identified through historic NHS data, invited through targeted outreach, and then enrolled through Sano Genetics’ digital platform, which supported e-consent, at-home saliva collection, appointment booking, and integrated data capture.
The study is important for several reasons.
First, it tests whether combining routine healthcare data with genetics can improve how researchers identify people at risk of liver disease earlier. That is directly relevant to the progression question Tim raises throughout the episode. If a patient has only mild abnormalities today, can added data help distinguish who is stable from who is likely to worsen?
Second, it shows that patients are willing to participate in this kind of model. The uptake described in both the conversation and the recruitment milestone announcement is striking, including very strong engagement with genetic testing. That matters because one of the common concerns in precision medicine studies is whether genetics will create friction for participation. In this case, the opposite appears to be true.
Third, it demonstrates a more scalable way to recruit participants into research. Instead of waiting for eligible patients to appear in the clinic, researchers can identify a broader pool systematically, reach out earlier, and guide them through a participant-friendly digital workflow. That has implications well beyond liver disease.
The conversation is especially relevant for sponsors and trial teams thinking about recruitment, enrichment, and endpoint strategy in metabolic disease.
Tim notes that traditional liver studies have often relied on biopsy-based models and inefficient recruitment processes. More recently, non-invasive methods such as FibroScan and imaging-based assessments have started to reshape the field. That creates an opportunity to rethink not only endpoints, but also how eligible participants are identified in the first place.
LiveWell points toward a model where routine data, genetics, and non-invasive assessment can work together as a recruitment funnel. Instead of screening out large numbers of unsuitable participants late in the process, studies could identify higher-probability candidates earlier based on longitudinal signals, genotype, and clinical context.
That could improve recruitment efficiency, reduce site burden, and make it easier to reach patients who have historically had less access to research opportunities. It also aligns with a broader move toward precision medicine, where the goal is not simply to find patients with a diagnosis, but to find the right patients for the right intervention at the right time.
Although Tim’s clinical focus is hepatology, he can see this approach extending beyond liver disease.
The same general principle applies in cardiometabolic disease, diabetes, renal disease, and potentially some cancers: subtle changes accumulate over time, and routine blood tests may hold early signals long before overt disease is diagnosed. Most current risk tools assess a patient at a single point in time. Tim argues that this misses the longer trajectory already visible in the data.
That idea has broad relevance for health systems and for research design. If longitudinal routine data can be used more effectively, it may become possible to identify risk earlier, intervene sooner, and build more efficient pathways into research across multiple therapy areas.
Listen to the full episode below.