As healthcare systems contend with aging populations and a rising burden of chronic disease, the limitations of current diagnostic approaches are becoming increasingly apparent.
Here, Russ Lebovitz, Chief Executive Officer and Cofounder at Amprion Diagnostics, and Arnon Chait, Chief Innovation Officer at Cleveland Diagnostics, discuss the need for more precise, biology-driven diagnostics – and how advances in biomarkers and proteomics could reshape patient care.
As populations age and chronic diseases become more prevalent, where do you see the biggest gaps in current diagnostic pathways for conditions such as cancer and neurodegenerative disease?
Russ Lebovitz: The biggest gaps in neurodegenerative diseases relate to identifying and using biomarkers that reflect the underlying disease across its stages. Ideally, these biomarkers should be detectable before clinical diagnosis, persist throughout progression, and decline when disease activity stops. Changes across biological samples – such as blood or cerebrospinal fluid – should correlate with disease activity.
Arnon Chait: Building on Russ’ point, many diagnostic pathways still rely on late-stage or indirect signals. In cancer care, intervention often occurs only after symptoms or structural changes appear, creating a reactive rather than proactive model.
There is also a broader issue: despite diagnostics guiding most clinical decisions, only a small proportion of healthcare spending is allocated to them. Current approaches can also struggle to distinguish clinically meaningful disease from low-risk findings, leading to both unnecessary procedures and delayed intervention.
A shift toward structured diagnostic pathways – using a sequence of tools with increasing complexity – could support earlier, more accurate, and cost-effective care.
What makes it so challenging to identify underlying disease biology at an early stage, particularly when patients may be asymptomatic or present with nonspecific findings?
AC: Early-stage disease is difficult to detect because biological changes occur before structural ones. Signals are often weak, and patients may be asymptomatic. Existing tools may also lack specificity, as they are not always directly linked to disease biology.
The key is developing diagnostics that reflect active disease mechanisms, rather than general abnormalities.
RL: In central nervous system (CNS) diseases, early stages are often clinically silent. Unlike cancer, where screening programs detect early or pre-disease states, similar frameworks are lacking in neurodegeneration.
Progress will depend on reliable biomarkers that identify both early disease and pre-disease states, while accounting for the complex, multi-pathology nature of these conditions.
How can emerging approaches help distinguish between clinically meaningful disease and benign or incidental findings?
AC: Approaches such as analyzing protein structure and detecting misfolded proteins aim to detect disease-specific signatures rather than just biomarker levels. This improves specificity and helps separate true disease signals from background variation, benign conditions, or incidental findings.
RL: There is a need for biomarkers that can distinguish between normal aging, early disease, active disease, and general injury responses. These markers should capture disease-related protein changes, including structural alterations (such as phosphorylation or mutations), misfolding or aggregation, and differences in protein levels. Advances in proteomics are now enabling these changes to be detected at scale, improving diagnostic precision.
What are the limitations of today’s diagnostics when it comes to risk stratification and guiding clinical decision-making in aging populations?
AC: Many diagnostics indicate abnormality but do not clarify risk or next steps, which is particularly problematic in aging populations, where comorbidities are common and not all findings are clinically significant.
At the same time, diagnostics play a critical but often underappreciated role in the efficient use of healthcare resources. Although they account for only around 2 percent of total healthcare spending, they inform an estimated 60-70 percent of medical decisions – and nearly all decisions in oncology.
This highlights an important opportunity: even modest improvements in how diagnostics are used across the care pathway could help clinicians better assess risk, guide next steps, and avoid unnecessary interventions, while also improving patient outcomes and reducing overall healthcare costs.
RL: In CNS diseases, there is a lack of validated disease-modifying biomarkers. Many early candidates were later shown to reflect normal aging or general injury, highlighting the need for rigorous validation across populations.
How might improved biological detection reduce unnecessary procedures, overtreatment, or prolonged diagnostic uncertainty for patients?
AC: All diseases involve changes in normal biology – the challenge is detecting those changes in a way that is specific to the disease process.
More precise diagnostics can give clinicians greater confidence in determining whether a finding represents meaningful disease, helping to avoid unnecessary escalation to invasive tests or treatments.
For patients, this may mean fewer unnecessary investigations, such as imaging or biopsies, less time spent waiting for a diagnosis, and clearer guidance on next steps. In this way, improved diagnostics support not only accuracy, but also more efficient and patient-centered care.
Looking ahead, what will “clinically actionable” diagnostics need to deliver over the next decade to truly impact patient management in chronic disease?
AC: Clinically actionable diagnostics must go beyond identifying abnormalities – they need to inform clinical decisions. This means delivering results that are disease- and patient-specific, reproducible, and practical within routine workflows.
Over the next decade, the most valuable diagnostics will help clinicians decide whether to biopsy, image, monitor, treat, or wait – and to do so with greater confidence. They will also need to be accessible across everyday care settings, not just specialized centers. Increasingly, these systems will integrate multiple data types and use AI to combine patient-specific information with broader clinical knowledge, enabling more informed and individualized care.
RL: For CNS neurodegenerative diseases, clinically actionable – or disease-modifying – diagnostics will need to deliver consistent and meaningful insights across the disease course. This includes enabling early and accurate diagnosis, as well as improving the ability to predict disease progression.
They will also play a key role in clinical research by supporting more precise patient selection and providing reliable endpoints to assess treatment efficacy. Together, these capabilities are essential for advancing both patient care and the development of new therapies.
Why will earlier and more precise disease identification become increasingly critical as the burden of chronic disease rises – and how should laboratories prepare for that shift?
AC: As chronic disease becomes more prevalent, healthcare systems will face growing pressure to diagnose earlier while using resources more efficiently. More precise detection can help target interventions toward patients most likely to benefit, while reducing unnecessary procedures for others.
Laboratories will be central to this shift. They will need to support advanced biomarker testing, integrate new assays into routine workflows, and deliver results that are both analytically robust and clinically meaningful.
RL: As disease-modifying biomarker tests for neurodegenerative diseases enter clinical practice, laboratories will need to scale their capabilities to meet increasing demand.
This will likely involve a combination of approaches – establishing in-house testing for lower-complexity assays that fit within existing workflows, while partnering with specialized laboratories for more complex tests that require manual processing and are not suited to high-throughput systems.
How is our understanding of neurodegenerative diseases evolving, and what does this mean for the future of diagnosis and treatment?
RL: One of the most important shifts in neurology is the move from symptom-based diagnosis to biology-based frameworks. As understanding of misfolded proteins and mixed pathologies grows, it is increasingly clear that many neurodegenerative diseases cannot be defined by a single mechanism.
Improving patient care and advancing therapies will depend on the ability to accurately detect and measure these underlying disease processes in living patients.
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