Biomarkers in Focus

BIOMARKERS IN FOCUS EEG Biomarkers: The Future of Monitoring Neurological Health? Advances in Nanomaterials for Detecting Disease Biomarkers The Evolution of Biomarkers in Modern Cancer Care Credit: iStock CONTENTS 4 Advances in Nanomaterials for Detecting Disease Biomarkers 8 EEG Biomarkers: The Future of Monitoring Neurological Health? 11 How Digital Biomarkers Enable Patient-Centric Clinical Trials 15 The Evolution of Biomarkers in Modern Cancer Care 19 The Landscape of Potential Biomarkers for ADHD 22 The Technique That Can Distinguish Up-Market Floral Honey 25 What Blood Biomarkers Can – and Can’t – Tell Us About Alzheimer’s BIOMARKERS IN FOCUS 3 TECHNOLOGYNETWORKS.COM FOREWORD Biomarkers are transforming science and healthcare by offering precise, measurable indicators of health, disease and quality. From molecular signatures that reveal the earliest stages of disease to chemical fingerprints that ensure product authenticity, novel biomarkers are reshaping diagnostics, treatment and monitoring. This eBook brings together a series of articles exploring the latest advances in biomarker research, spanning fields such as neurology, oncology, food safety and clinical trial design. Through expert insights and real-world examples, this collection highlights practical applications, emerging challenges and opportunities for innovation in biomarker research. The Technology Networks editorial team 4 BIOMARKERS IN FOCUS Advances in Nanomaterials for Detecting Disease Biomarkers Alex Beadle The early diagnosis of disease is one of the most important factors in a patient’s clinical treatment. Depending on the type of disease, an early diagnosis may broaden the range of potential treatment options available, increase the opportunities for informed decision-making and may even allow for the deployment of preventative measures that can significantly improve a patient’s quality of life and odds of making a full recovery. The non-invasive detection of disease-related biomarkers plays a key role in improving the early diagnosis of disease. In recent years, biosensors capable of detecting biomarkers in bodily fluids have stood out as a key technology in this field, largely thanks to their cost-effectiveness, portability and rapid response times. Advanced biosensors use nanomaterials to improve their sensitivity and boost signal amplification. Functionalized nanomaterials can also be used to further enhance sensor properties such as resolution, response time, linearity, repeatability, robustness, biocompatibility and stability. This article explores the discovery and continued development of the nanotechnologies that are underpinning advancements in biomarker sensing today. How do biosensors work? Biomarkers – a portmanteau of the phrase “biological marker” – are measurable indicators that can be used to assess some kind of biological state or condition. Examples of biomarkers include everything from a person’s blood pressure and heart rate to genetic markers and other molecules that can be observed using modern laboratory science. Credit: iStock/volodyar TECHNOLOGYNETWORKS.COM BIOMARKERS IN FOCUS 5 Two of the most widely used laboratory methods for biomarker detection and quantification are enzymelinked immunosorbent assay and polymerase chain reaction methods. While these techniques do have many advantages – including high sensitivity, selectivity and repeatability – they are somewhat limited by their long response times and their consumption of reagents required for every analytical run. This makes them a less practical option for continued monitoring or for rapid biomarker detection for point-of-care applications. Biosensors are a leading alternative for biomarker detection. A biosensor is made up of two main components: the biosensing component that binds to the target biomarker, and a transducer that converts this binding event into a measurable signal for detection. There are a wide variety of different transducer systems used in biomarkers, including electrochemical, optical, gravimetric, colorimetric and field-effect transistorbased sensors. Advanced nanomaterials push the detection limits of biosensors In recent years, the use of nanomaterials has become a major focus of biosensor development. Because of their unique structures and small size, nanomaterials often display unique electrical, optical, magnetic and thermal properties that can help to enhance a particular biological signal and improve the sensitivity of a biosensor. For example, functionalized nanomaterials are frequently used as a substrate modifier in biosensors, as their high surface-to-volume ratio makes them an ideal surface for immobilizing analytes of interest. This effectively increases the density of the biomarker molecule, improving the overall sensitivity of the biomarker. There are many other mechanisms by which nanomaterials can help enhance the function of biosensors and many different nanomaterial classes have been effectively utilized in biosensing. Metallic nanoparticles Noble metal (i.e., gold, silver and platinum) nanoparticles are used extensively in many industrial and scientific applications, such as catalytic converters, due to their good stability and ease of modification. Gold nanoparticles (AuNPs) are of particular interest for optical and electrochemical biosensors. This is largely due to their high conductivity, biocompatibility, stability and the ease with which their surface can be modified by thiol groups. Optical biosensors Optical biosensors work by detecting changes in light signals (intensity, frequency, polarization) as a light source interacts with a biorecognition element that has bound to a biomarker. Optical biosensors are the most common class of biosensor. Electrochemical biosensors Electrochemical biosensors make use of changes in electrical properties (current, potential, impedance, etc.) that can occur when a bioreceptor and an analyte interact. In electrochemical biosensing, AuNPs can be advantageous when the target biomarker molecules are present in small amounts, and thus the electrochemical signals are relatively weak. The very high electrical conductivity of AuNPs allows for the electrochemical signals from such redox reactions to still be detected with high sensitivity and selectivity. Noble metal nanoparticles are also favored in optical biosensors as their colors can be detected with the naked eye. Their color may also change depending on their state, which again is of use in biosensing. For example, researchers have used surface-functionalized TECHNOLOGYNETWORKS.COM BIOMARKERS IN FOCUS 6 AuNPs in biosensing platforms to detect the SARSCoV- 2 coronavirus, relying on the red-to-purple color change that occurs when AuNPs begin to aggregate together around a virus particle. Noble metal nanoparticles have also been used for viral pathogen detection in electrochemical biosensors, including for viruses such as SARS-CoV-2, human immunodeficiency virus, hepatitis, influenza and Zika virus. Carbon-based nanomaterials Carbon nanomaterials are another popular type of nanomaterial that is commonly incorporated into optical and electrochemical biosensing platforms. The unique structure of carbon nanotubes (CNTs) makes them of great interest for biomarker sensing applications. The tubular structure results in a very high surface area-to-volume and aspect ratio, while retaining good chemical stability and electrical conductivity. In electrochemical biosensors, CNTs can be used to increase the active surface areas of the electrodes and improve electron transfer, which can increase the sensitivity and lower the limit of detection for electrochemical sensors. Modified CNTs have been demonstrated in electrochemical biosensors that can quantify multiplex biomarkers in human blood serum for cancer diagnosis. Multiplex biomarker analysis Multiplex assays can simultaneously measure multiple relevant biomarkers in a single sample, providing a more comprehensive view of the biological processes at play. Depending on their physical structure, CNTs may also have fluorescent properties that can be leveraged for fluorescence-based optical biosensors. Graphene – a two-dimensional sheet of carbon atoms arranged in a lattice – is a useful material for wearable biosensors, where graphene’s biocompatibility and flexibility make it uniquely suitable for enhancing performance in wearable device electrodes. Grapheneenhanced electrochemical sensor electrodes are a potential solution for the challenges of real-time health monitoring and personalized medicine, with this technology already having been demonstrated in the real-time non-invasive tracking of biomarkers relating to inflammation, diabetes, gout and heart diseases. Quantum dots Quantum dots (QDs), sometimes known as “zerodimensional” materials, are traditionally semiconductor particles (though can also be made of graphene or regular carbon) measuring less than 10 nanometers in size, and made up of just a few thousand atoms in total. At this small size, QDs are strongly affected by quantum phenomena that play a significant role in how the dots absorb and emit light. The fact that their behavior can only be explained by quantum physics – not the laws of classical physics – has led to QDs being explored for a vast range of different applications, including in medicine, batteries, catalysis and as a component of biomarker-detecting biosensors. Their strange light absorption properties make QDs a prime target for use in optical fluorescence biomarker detection. QDs can be conjugated with antibodies, aptamers or other molecules that will target a specific biomarker and act as light-emitting probes for in vitro assays. Compared to traditional options for fluorescent dyes, QDs are advantageous as they do not suffer from the same issues of photo-bleaching and they can be easily tuned to emit different wavelengths of light. While research into QDs is still in its relative infancy, biosensing devices that use QDs have been developed for detecting biomarkers relating to cardiovascular disease, tuberculosis and breast cancer. TECHNOLOGYNETWORKS.COM BIOMARKERS IN FOCUS 7 Challenges and opportunities in nanotechnology Nanomaterials have a very particular appeal for enabling better biomarker analysis. There is a diverse range of different materials that offer unique size- and shapedependent properties that could be used to enhance analytical biosensors. The potential for nanomaterials to enable rapid, cost-effective, portable biomarker test kits is an attractive prospect for those looking to improve point-of-care diagnostics and continued health monitoring in individuals suffering from serious diseases or cancers. While the future outlook for these systems is bright, there are still several challenges that these nanomaterial-enabled biosensors must overcome. Firstly, while there are some examples of commercially available biosensors that feature nanotechnology, the majority of this type of biomarker biosensor development is in the laboratory research phase and must still be proven to be commercially viable. One aspect of this challenge is that synthesizing and characterizing these nanomaterials often requires the efforts of very skilled technicians. Ensuring the reproducibility and stability of nanoparticle-based sensors as they are produced and used in real-world situations is also key; for real ease-of-use, a sensor must be stable enough to analyze whole blood without any additional sample dilution or processing, for example. With portability being a desired feature, it is also important that these technologies can demonstrate a good shelf-life within reasonable expected temperature conditions. Despite these challenges, innovation continues to drive increasingly novel nanomaterials and new ways of detecting and quantifying disease-related biomarkers. With the advancement of these technologies, scientists hope to further improve point-of-care and personalized diagnostics to deliver the best possible outcomes for patients. Credit: iStock/Love Employee 8 BIOMARKERS IN FOCUS EEG Biomarkers: The Future of Monitoring Neurological Health? Molly Coddington According to a 2024 study, over 1 in 3 individuals are affected by neurological conditions worldwide, with the overall disability-adjusted life years caused by neurological conditions increasing by 18% since 1990. As the global burden of these conditions – such as stroke, dementia and epilepsy – rises, there is a growing urgency to be able to diagnose them earlier and with greater accuracy. Dr. Javier Escudero, reader in biomedical signal processing at the University of Edinburgh, has published several papers exploring and discussing the potential role of the electroencephalogram (EEG) as a biomarker for brain disorders, including Alzheimer’s disease. Technology Networks had the pleasure of interviewing Escudero, who discussed how the EEG offers a unique window into real-time brain activity, why its affordability, portability and non-invasiveness make it a strong candidate for remote monitoring and potential global health applications. Q: Can you explain why a wider variety of biomarkers could help to diagnose neurological disorders, such as neurodegenerative diseases? A: Neurodegenerative conditions, such as Alzheimer’s disease, are very complex and typically develop over many years. A variety of changes take place in the brain, Credit: iStock/janiecbros TECHNOLOGYNETWORKS.COM BIOMARKERS IN FOCUS 9 and not all of these changes happen at the same time. A single test or scan may not be able to capture this variety of processes, and it may not be able to accurately reflect the evolution of the disease across all its stages. Having a variety of biomarkers provides more options for clinical experts to understand what is going on in the disease process. Biomarkers capture different phenomena of the evolution of the disease. For example, an imaging scan may show whether some regions of the brain have shrunk or whether there is a localized lesion. A spinal fluid test may pick up a buildup of abnormal proteins in the brain. These processes happen at different stages in the disease. Having diverse biomarkers may also help to distinguish different types of neurological conditions. The ultimate aim is to diagnose these conditions earlier so that, hopefully, disease-modifying drugs can slow disease progression. Q: What is an EEG, and how might it be used in clinical practice/ research? A: An EEG is a test that measures the small electric fields generated by neurons, the brain’s nerve cells. Each neuron produces a tiny electrical current when it becomes active. When enough nearby neurons become active together, the electric field becomes large enough so that it can be detected non-invasively, over the scalp, with appropriate electrodes on the skin. The voltages captured by these electrodes are then magnified and recorded by a computer. It is typical to place 20 or so electrodes on the scalp to capture fields generated by different brain regions, but, depending on the application, this number of electrodes could be lower or much higher. In this way, the patterns captured in the EEG can tell us a lot about how well the brain functions. It is common to use the EEG in clinical practice to diagnose and monitor conditions such as epilepsy. Though the EEG is not yet accepted as a biomarker for dementia, or Alzheimer’s disease in particular, there is increasing interest in this application. There is evidence that Alzheimer’s disease disrupts how different brain regions communicate with each other. There is also increasing interest in enabling the use of EEG as a remote screening and/or monitoring tool. The EEG is non-invasive, affordable in comparison with other neuroimaging techniques and portable. This makes the EEG an ideal candidate to assess brain activity remotely, away from hospitals. This could be valuable, for example, to monitor epilepsy at home or to assess brain damage after an accident. Q: Are there research or clinical examples where the EEG has helped identify brain-activity-based biomarkers for specific diseases or conditions? A: Yes. A notorious example is epilepsy, where EEG is used in clinical practice to determine the presence and type of abnormal electrical bursts in the brain that lead to seizures. " This makes the EEG an ideal candidate to assess brain activity remotely, away from hospitals. This could be valuable, for example, to monitor epilepsy at home or to assess brain damage after an accident." TECHNOLOGYNETWORKS.COM BIOMARKERS IN FOCUS 10 Q: What are the core challenges that exist in this research field? A: In the past, the field of EEG analysis has been held back by a lack of large datasets and standardization procedures to ensure that EEG data acquired at different labs can be analyzed together. However, this is changing rapidly. Several research groups and consortia are now sharing EEG data. This facilitates research and helps ensure that the findings can be generalized to different settings, importantly low- and middle-income countries. Progress is being made, but there is still work to be done. Beyond the availability of data itself, it is important to ensure that the analysis of EEG that would lead to a potential biomarker is reliable and not influenced by external factors that may vary from day to day. It is also important to ensure that any potential biomarker is not only sensitive enough to pick up that something abnormal is going on in the brain, but it is also specific enough to inform about what the cause of the abnormality may be. Q: What do you think the future of brain biomarker research could look like? A: I envisage “multimodal” biomarkers to inform clinicians about the “brain health” of a person. These multimodal biomarkers would combine, with the help of artificial intelligence, information obtained from neuroimaging, blood tests, EEG and other methods with the personal clinical history of the individual. In this way, they would assist clinicians in achieving patientspecific early diagnosis of neurological conditions, such as Alzheimer’s disease. I think that the EEG has an important role to play in this future, given its non-invasiveness and affordability, which enables the possibility to acquire EEG data at home or in the community, even in combination with data about the performance of the person on certain tasks (such as games on tablets or phones). The wealth of longitudinal data acquired in this way would enable us to detect promptly very subtle changes in brain function caused by neurological diseases, even before serious symptoms arise. MEET THE INTERVIEWEE: Dr. Javier Escudero is a reader in biomedical signal processing at the Institute for Imaging, Data and Communications in the School of Engineering of the University of Edinburgh, UK. His research interests are in the development and application of signal processing and machine learning algorithms for physiological signals. Credit: iStock/LightFieldStudios 11 BIOMARKERS IN FOCUS How Digital Biomarkers Enable Patient-Centric Clinical Trials Blake Forman From smartphones to wearables, digital devices have become a key aspect of everyday life. These technologies can collect an array of data about a person, such as their fitness level and mood. Harnessing these devices in clinical trials to capture digital biomarkers has the potential to enable more adaptive, patientcentric trials. The increasing amount and variety of data collected by digital devices, alongside advancements in the miniaturization of electronics, have created new possibilities for integrating digital biomarkers into clinical trials. These measures have several advantages over traditional clinical outcome assessments and can be useful in trials where accurate and timely data collection is crucial for assessing investigational treatments. “Digital biomarkers can be integrated into clinical trials in several ways: as exploratory endpoints to understand physiological correlates of disease activity or treatment response, as stratification tools to identify high-risk participants or even as surrogate endpoints of treatment response,” Dr. Robert Hirten, associate professor of medicine, artificial intelligence and human health at What are digital biomarkers? Digital biomarkers are objective, quantifiable indicators of health, disease or treatment response collected and measured by digital devices such as smartphones, wearables and implants. Examples of digital biomarkers include heart rate variability from a smart watch and recorded speech patterns to track mood. Credit: iStock/Khanchit Khirisutchalual TECHNOLOGYNETWORKS.COM BIOMARKERS IN FOCUS 12 the Icahn School of Medicine at Mount Sinai, told Technology Networks. Digital biomarkers can be used in tandem with traditional trial measures, such as self-reports or clinical interviews. This enables the collection of objective, continuous data reflecting participants' real-world experiences, which complements standard trial endpoints. “This can result in a more precise assessment of how a treatment affects daily functioning and behavior,” Dr. Nick Jacobson, associate professor in biomedical data science, psychiatry and computer science at the Geisel School of Medicine at Dartmouth, told Technology Networks. “It's possible that digital biomarkers could detect treatment responses or subtle changes more quickly or sensitively than conventional methods, potentially leading to more efficient trial designs,” Jacobson continued. “They also provide valuable information on how a treatment impacts everyday life outside the clinic setting.” Digital biomarkers of mental wellbeing and gut health Many areas of medicine could benefit from the implementation of digital biomarkers. In clinical trials for neurological disorders, cognitive and motor functions are traditionally assessed with specific tests, clinical interviews or through self-reporting of symptoms. Digital biomarkers can provide a non-invasive and objective measurement that more accurately represents how a patient is responding to a treatment. Smartphones and wearables offer significant advantages to the field of mental health. “They allow for continuous, objective monitoring of behavior and physiology using passive sensors like accelerometers and GPS right in people's daily lives,” explained Jacobson. This data stream allows for the development of digital biomarkers that “can assist in predicting and detecting mental health conditions such as anxiety and depression earlier than might otherwise be possible.” Beyond clinical trials, wearables and sensors that collect digital biomarkers offer a range of benefits for monitoring and treating mental health conditions. “For monitoring, these tools provide a highly detailed, real-time view of symptom fluctuations, offering insights that are difficult to capture through infrequent clinic visits or memory-based self-reports,” Jacobson said. “When it comes to treatment, this continuous data “It's possible that digital biomarkers could detect treatment responses or subtle changes more quickly or sensitively than conventional methods, potentially leading to more efficient trial designs,” Jacobson continued. “They also provide valuable information on how a treatment impacts everyday life outside the clinic setting.” TECHNOLOGYNETWORKS.COM BIOMARKERS IN FOCUS 13 stream supports the creation of personalized, just-intime adaptive interventions. Such systems can identify moments of increased need or risk and deliver targeted support right when it could be most helpful.” Jacobson and colleagues have developed a generative AI therapy chatbot called Therabot. This is designed to provide real-time support for the many people who lack regular or immediate access to a mental health professional. “Digital tools, including smartphone applications and AI-driven platforms like the Therabot system developed in my lab, can deliver evidence-based assessment and therapeutic interventions at scale. This reaches individuals who might otherwise struggle to access traditional care due to cost, location or stigma,” stated Jacobson. The use of wearables to collect digital biomarkers has applications for a range of diseases beyond mental health conditions. “Wearable technology offers a potentially new approach to managing IBD [inflammatory bowel disease] by enabling continuous, non-invasive monitoring of patients in their daily lives,” explained Hirten. In a recent study, Hirten and colleagues evaluated how several physiological metrics are associated with IBD flares. They found that circadian patterns of heart rate variability identify such inflammatory instances. In addition, changes in these metrics can identify and precede flares of IBD by up to seven weeks. “Although further research is needed, our hope is that this can give patients and their doctors a critical window to intervene early,” said Hirten. “Beyond flare prediction, wearables hold promise in being able to monitor treatment response, identifying ongoing symptoms and inflammation – all outside the traditional confines of the clinic.” Challenges in integrating digital biomarkers in clinical trials As outlined in the examples above, the potential of digital biomarkers to advance many aspects of human health is immense. However, there are still challenges that need to be overcome for them to see more regular use in clinical trials. One of the primary concerns is around data privacy, given the sensitive nature of the data collected. This is in addition to other challenges that still need to be evaluated, “Including ensuring data quality, validating digital measures against gold standards and navigating evolving regulatory frameworks,” said Hirten. AI and machine learning have the potential to help overcome some of these challenges by improving data quality and integrity. “AI and machine learning are central to extracting meaningful insights from the high-volume data generated by wearables. These tools can identify subtle patterns and temporal trends that are not discernible through traditional analysis,” Hirten explained. By learning an individual’s unique physiological baseline and deviations from it, AI can enhance the accuracy and clinical relevance of data collected using wearable devices. “AI also drives personalization; my research, supported by the National Institute of Mental Health, indicates that deep learning models tailored to an individual's unique data streams often provide the most accurate predictions of symptom changes, paving the way for highly personalized mental healthcare,” stated Jacobson. “AI can also help by integrating data from multiple sources, like sensors and self-reports, to build a more complete picture of an individual's mental health.” Given the data security and regulatory challenges that come with integrating digital biomarkers in clinical trials, Hirten recommends that “A thoughtful, methodologically rigorous approach is essential to unlock their full potential.” TECHNOLOGYNETWORKS.COM BIOMARKERS IN FOCUS 14 Future outlooks for digital biomarkers in clinical trials Digital biomarkers offer substantial value in clinical trials by providing objective, continuous and realtime data on participants. “In the future, I believe digital biomarkers will become integral to the design and execution of clinical trials. They will enable more adaptive, patient-centric trials—where real-world data informs eligibility, dosing and endpoints in real time,” said Hirten. Continued advancements in AI and machine learning are set to further enhance the accuracy of digital biomarkers. Moreover, digital biomarkers could improve patient engagement by integrating other digital technologies such as remote monitoring and AI chatbots. “We’ll also see digital biomarkers supporting broader inclusion by potentially reducing the need for frequent site visits, which is especially valuable for participants in remote or underserved areas,” Hirten concluded. “As regulatory standards evolve and validation frameworks mature, digital biomarkers may transition from exploratory tools to accepted clinical endpoints, helping us bring more precise and timely therapies to patients with chronic diseases.” ABOUT THE INTERVIEWEES Dr. Nicholas Jacobson is an associate professor of biomedical data science, psychiatry and computer science at Dartmouth, where he directs the AI and Mental Health: Innovation in Technology Guided Healthcare (AIM HIGH) Lab in Dartmouth’s Center for Technology and Behavioral Health. His research leverages technology – particularly AI and data from smartphones and wearable devices – to enhance the assessment and scalable treatment of anxiety, depression and related conditions. Dr. Robert Hirten is an associate professor of medicine, artificial intelligence and human health at the Icahn School of Medicine at Mount Sinai. Hirten’s research focuses on the use of connected devices and digital technology for the study of health and disease. Leveraging this technology, he leads wearable device studies across patient populations and disease states. His work has resulted in the development of novel machine learning algorithms derived from wearable devices for the prediction of various health and disease endpoints. 15 BIOMARKERS IN FOCUS The Evolution of Biomarkers in Modern Cancer Care Isabel Ely, PhD Cancer biomarkers are biological indicators found in blood, body fluids or tissues that signal the presence of abnormal processes or diseases. In oncology, these markers offer more than just cancer identification – they provide critical insights into disease progression, recurrence risks and treatment outcomes. Tumor markers can serve as prognostic indicators, forecasting the disease’s course, whereas predictive biomarkers can help guide treatment responses. Classified by proteins like enzymes, hormones and receptors, cancer biomarkers also emerge from genetic changes – mutations, amplifications and translocations – that help categorize cancer types and guide therapy. For effective clinical use, cancer biomarkers must be detectable through reliable, cost-efficient methods that improve patient outcomes. The use of biomarkers in cancer care has expanded dramatically, offering new avenues for earlier diagnosis, improved treatment selection and more accurate prognostic predictions. The evolution of cancer biomarkers in precision oncology In the era of precision oncology, biomarkers are redefining how we detect, diagnose and treat cancer. Unlike traditional approaches, which often rely on population-level trends and non-specific clinical signs, biomarker-guided strategies enable a more individualized, predictive and dynamic model of cancer care. Credit: iStock/angelp TECHNOLOGYNETWORKS.COM BIOMARKERS IN FOCUS 16 “Biomarkers have fundamentally reshaped how we understand cancer,” Professor Samra Turajlic, group leader and consultant medical oncologist at The Francis Crick Institute and The Royal Marsden Hospital, told Technology Networks. “Historically, we treated cancers by site and appearance; now, we can probe the biological processes driving each tumor.” Traditional diagnostic tools, such as imaging or histopathological evaluation, often detect cancer only after it has progressed to a detectable size or caused symptoms. In contrast, biomarkers – especially those detectable in blood or other biofluids – can signal the presence of malignancy before clinical symptoms appear. For example, a previous study used cell-free DNA methylation patterns for multi-cancer early detection, achieving a specificity of over 99% across more than 50 cancer types, with promising sensitivity even for stage I cancers. “Biomarkers have allowed us to identify specific mutations, immune profiles or metabolic shifts that give us clues about how a cancer emerged, how it behaves and how it might respond to treatment,” said Turajlic. “Importantly, they’ve helped reveal that cancer isn’t a static disease; it evolves, and our biomarkers must evolve with it.” One significant advantage of biomarkers is their ability to guide targeted therapy. Traditional treatments such as chemotherapy are often applied broadly, with varying success and considerable toxicity. Biomarkers, however, enable clinicians to match patients with therapies likely to be most effective for their specific tumor profile. The landmark use of epidermal growth factor receptor mutation diagnosis in guiding treatments for non-small cell lung cancer illustrates this: patients with these mutations respond dramatically better to tyrosine kinase inhibitors than to standard chemotherapy. Similarly, the presence of PD-L1 expression or tumor mutational burden (TMB) has become instrumental in predicting response to immune checkpoint inhibitors. In the KEYNOTE-158 study, pembrolizumab showed durable responses in patients with high TMB across various tumor types, leading to the US Food and Drug Administration's (FDA) approval of the biomarker as a companion diagnostic. Biomarkers can be classified into diagnostic, predictive and prognostic categories, each with distinct roles in enhancing cancer management. “Biomarkers have allowed us to identify specific mutations, immune profiles or metabolic shifts that give us clues about how a cancer emerged, how it behaves and how it might respond to treatment,” said Turajlic. “Importantly, they’ve helped reveal that cancer isn’t a static disease; it evolves, and our biomarkers must evolve with it.” TECHNOLOGYNETWORKS.COM BIOMARKERS IN FOCUS 17 Diagnostic Diagnostic biomarkers are crucial for identifying cancer in its early stages – when treatment is most effective. These biomarkers typically identify molecular changes associated with cancer cells, making it possible to detect cancer before symptoms appear. Circulating tumor DNA (ctDNA) was identified by Diaz and colleagues as a promising diagnostic biomarker for the early detection of colorectal cancer. Their research demonstrated that ctDNA analysis could detect cancer-related mutations in plasma samples, offering a less invasive and highly sensitive method for early diagnosis, potentially replacing traditional tissue biopsies. This study laid the groundwork for ongoing efforts to integrate ctDNA-based tests into routine cancer screening. Predictive Predictive biomarkers help clinicians predict how patients will respond to specific therapies, allowing for more personalized treatment plans. These biomarkers provide information on the likelihood that a patient will respond to a particular drug or treatment regimen, thus optimizing therapeutic outcomes. A well-known example of a predictive biomarker is the use of the HER2 gene amplification status in breast cancer. A pivotal study by Slamon and colleagues demonstrated that patients with HER2-positive breast cancer (those with overexpression of the HER2 protein) were more likely to benefit from trastuzumab (Herceptin™), a targeted therapy. This finding led to the FDA's approval of trastuzumab for HER2-positive breast cancer, revolutionizing treatment and improving survival rates. Testing for HER2 status is now a routine part of breast cancer diagnosis and treatment planning Prognostic Prognostic biomarkers provide insight into a patient’s likely disease course, irrespective of treatment, helping clinicians assess the aggressiveness of the cancer and predict patient outcomes. These biomarkers are essential for determining the best course of action, particularly when choosing between treatment regimens or deciding whether active surveillance is appropriate. A notable example of prognostic biomarker use is the Oncotype DX test for breast cancer – a gene expression test that analyzes a panel of 21 genes to assess the risk of recurrence in early-stage breast cancer. The TAILORx trial demonstrated that patients with low Oncotype DX scores could safely avoid chemotherapy, sparing them from unnecessary side effects without compromising their survival. The study significantly influenced how breast cancer treatment is personalized, demonstrating how prognostic biomarkers can improve decision-making. Identifying and validating reliable cancer biomarkers is a complex and multi-faceted challenge that continues to hinder advancements in personalized oncology. “Cancer is profoundly heterogeneous, not just between patients, but within a single patient’s tumor,” Turajlic explained. “This means that a single biopsy might not capture the full biological landscape. That’s compounded by sample variability and assay heterogeneity; different labs, platforms and protocols can yield different results even when analyzing the same biomarker in the same patient.” Even more challenging is the ability to differentiate between benign and malignant conditions using a single marker or a set of biomarkers. Research has shown that the genetic and molecular diversity between tumors is a key obstacle in identifying biomarkers universally applicable across different cancer subtypes. “There’s a tendency to treat biomarkers as static indicators, when, in fact, tumors evolve – especially under the selective pressure of treatment. Without dynamic, longitudinal measures, we risk missing the biological shifts that are most clinically relevant.” Even after a potential biomarker is identified, validating its clinical utility is a lengthy and rigorous process. It must demonstrate not only that it can accurately detect or predict disease but also that it can improve patient TECHNOLOGYNETWORKS.COM BIOMARKERS IN FOCUS 18 outcomes. This requires large-scale clinical trials to confirm the biomarker’s sensitivity, specificity and reproducibility in real-world settings. Furthermore, biomarkers must meet regulatory standards set by bodies, such as the FDA, a process that can be slow and resource-intensive. Turajlic explained that this often leads to a slow and uneven translation into clinical research, often due to underestimating the complexity of the disease and overestimating what a single biomarker can tell us. “Most current technologies provide a static view, usually from a single biopsy at one time point. But cancer is not static. That’s a huge limitation.” Despite the promising strides in cancer biomarker research, several challenges must be addressed to maximize their clinical impact. Future research must focus on overcoming the complexities posed by intra-tumoral heterogeneity, which complicates the identification of universally applicable biomarkers. Developing biomarkers that are consistently present across various types of cancer and stages, as well as distinguishing between malignant and benign conditions, will be crucial for improving diagnostic accuracy. Turajlic and her team are looking to progress the use of biomarkers in cancer research by conducting the Multiomic Analysis of Immunotherapy Features Evidencing Success and Toxicity (MANIFEST) project – for which Turajlic is the project’s lead. “The MANIFEST project is a national effort to build a sustainable, National Health Service-embedded platform for deep genotyping and phenotyping, specifically focused on understanding what drives patient response, adverse effects and resistance to immunotherapy,” she explained. As part of the MANIFEST project, Turajlic and the team are collecting rich, longitudinal data from patients, including multiomic profiles from tissue, blood and stool, so they can identify biological signatures that predict both efficacy and toxicity of immunotherapy. “One of our central aims is to move away from single biomarkers and instead build integrated models that consider multiple biological layers and time points. We’re trying to move towards composite, multiomic biomarkers where we integrate genetic, transcriptomic, proteomic and clinical data into a more holistic signature that reflects both the tumor and its environment. That’s what will ultimately enable more tailored and effective treatment strategies,” Turajlic detailed. Moreover, advancements in technologies like liquid biopsies and single-cell analysis hold great promise in revolutionizing biomarker detection. Non-invasive methods for continuous monitoring of tumor evolution, such as ctDNA analysis and minimal residual disease detection, will play a key role in early detection, monitoring and personalized treatment strategies. “We're now seeing progress with single-cell sequencing, spatial transcriptomics and liquid biopsy, all of which give us a more dynamic picture,” she said. “These technologies can help us track clonal evolution, monitor treatment response and identify emerging resistance. “The challenge now is integrating these technologies into routine care in a way that’s scalable, cost-effective and clinically meaningful. That’s where collaboration between researchers, clinicians and data scientists becomes crucial,” Turajlic concluded. ABOUT THE INTERVIEWEE Professor Samra Turajlic is a group leader and consultant medical oncologist at The Francis Crick Institute and The Royal Marsden Hospital. She completed her undergraduate studies at Oxford University and her clinical training at University College London Medical School. She gained a PhD in 2013 from the Institute of Cancer Research in the field of melanoma genetics and targeted therapy resistance. Turajlic is the chief investigator of translational studies into melanoma and kidney cancer (TRACERx), and her research goal is to develop an evolutionary understanding of cancer for patient benefit. 19 BIOMARKERS IN FOCUS The Landscape of Potential Biomarkers for ADHD Katie Brighton Attention deficit/hyperactivity disorder (ADHD) is a developmental condition that is characterized by an ongoing pattern of inattention, hyperactivity or both. It is one of the most common disorders in children and affects approximately five percent of children worldwide. In 40-50% of cases, the disorder persists in adults. The clinical presentation and symptom profile of ADHD vary greatly from individual to individual, and co-morbidities including autism spectrum disorder, intellectual disability and learning disorders such as dyslexia are common. The current diagnostic process is based on behavioral tests and interviews prescribed by the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition. This form of diagnosis can prove challenging, particularly in cases with predominantly inattentive presentation, when co-morbidities are present or for the female sex. “Currently, ADHD diagnosis relies exclusively on clinical presentation and patient history, underscoring the need for clinically relevant, reliable and objective biomarkers,” Sheng-Yu Lee, Liang-Jen Wang and Cheng-Fang Yen said in their recent work. Biomarkers could form a more objective and accurate means of diagnosing ADHD. This listicle reviews potential biomarkers for ADHD, including neuroendocrine markers, genetic markers and metabolite markers. Potential neuroendocrine biomarkers Yen and colleagues propose that the neuroendocrine system, which is closely linked to age and sex, may influence the development of neural circuitry and Credit: iStock/Ivan Bajic TECHNOLOGYNETWORKS.COM BIOMARKERS IN FOCUS 20 therefore ADHD. The sex-based differences in the neuroendocrine system may account for the higher prevalence of ADHD in males. Their research focused on the most common steroid in the human body, dehydroepiandrosterone sulfate (DHEA-S), which is produced by the adrenal cortex and the brain. In their study, people with ADHD had lower levels of DHEA-S compared to controls, and DHEA-S levels were negatively correlated with impulsivity. In boys with ADHD, the researchers found that polymorphisms in the STS gene, which influences androgen synthesis and metabolism and has previously been associated with increased ADHD risk, were linked to DHEA S levels. This indicates that STS gene polymorphisms could contribute to the pathology of ADHD and form an ADHD biomarker. Potential genetic biomarkers The first wave of genetic studies into ADHD focused on genes involved in dopaminergic neurotransmission, in keeping with the hypothesis that ADHD may be, in part, caused by altered dopamine signaling. The variable number of tandem repeats in the 3’-untranslated region of the dopamine transporter gene, DAT1, has been identified as a potential biomarker for ADHD, with the 10-repeat variant having been associated with childhood ADHD, and the 9-repeat variant more strongly linked to adulthood ADHD. A seven-repeat allele of the dopamine D4 receptor gene, DRD4, is also associated with ADHD in children, with neuropsychological and neuroimaging studies linking this variant with worsened attention and impulsivity. Altered Wnt signaling, which orchestrates cell proliferation and differentiation, has also been linked to neurodevelopmental disorders including ADHD. LRP5 and LRP6 code for essential Wnt signaling activation receptors, and research has indicated that a variant of LRP5 is associated with childhood ADHD in girls, and a variant of the LRP6 gene is associated with ADHD in boys. More recently, a genome-wide association study outlined that many genetic variants combine to increase the risk of ADHD, finding variants across 12 different loci that significantly differ between individuals with ADHD and neurotypical controls. Potential metabolite biomarkers Metabolites are small molecules formed during biochemical reactions, which encompass amino acids, lipids, hormones and exogenous substances that are metabolized by humans. Altered levels of specific metabolites in blood have previously been linked to neuropsychiatric conditions including dementia and bipolar depression. Shi and colleagues used Mendelian Randomization – a technique used to infer which genes cause disease – and genome-wide association study data to analyze the genetic link between plasma metabolites and ADHD. Their research identified 42 metabolites with a causal effect on ADHD, 22 of which were positively associated with ADHD risk. The metabolic pathways implicated in ADHD risk included long-chain polyunsaturated acid biosynthesis, and methionine, tyrosine, cysteine and taurine metabolism. A particular metabolite of interest was 3-methoxytyramine sulfate (MTS), a product of tyrosine metabolism and a precursor to the neurotransmitter dopamine. Shi and colleagues noted that higher levels of MTS were associated with a reduced risk of ADHD and that MTS could act as a biomarker of neurotransmitter activity. Another study by Hung and colleagues pinpointed 156 metabolites that differed between children with ADHD and controls. Of these, children with ADHD had notably increased cholic acid and homoveratric acid levels and decreased levels of inosine and nicotinuric acid. TECHNOLOGYNETWORKS.COM BIOMARKERS IN FOCUS 21 The discovery of metabolites that differ between people with ADHD and controls could provide insights into ADHD pathophysiology and may lead to a metabolite panel that can be used for diagnosis. Potential neurological biomarkers Magnetic resonance imaging (MRI) research has illustrated that the brains of people with ADHD are structurally different to neurotypical controls. In the brains of people with ADHD, the volume of gray matter is reduced and the maturation of cortical and subcortical regions is delayed. Electroencephalography has also been used to reveal differences between specific brainwave patterns that are associated with ADHD severity, indicating that neuroimaging techniques have potential for detecting ADHD biomarkers. Functional MRI (fMRI) provides better spatial resolution than MRI, offering greater insights into the brain regions and networks that are linked to ADHD. Zamanzadeh and colleagues used fMRI to identify potential diagnostic biomarkers for ADHD, focused on audiovisual integration (AVI) networks due to the association between ADHD and sensory processing deficits. They examined the brain regions with an established role in AVI and assessed the organization and efficiency of the AVI-related brain networks, finding significant differences in connectivity patterns between controls and people with ADHD. Functional near-infrared spectroscopy (fNRI), a non-invasive brain functional imaging technique, has identified neurobiological features of ADHD that have potential for use as a diagnostic biomarker. Zhou and colleagues used the results from fNRI scans to identify that children with ADHD exhibit abnormal activation in the right inferior frontal gyrus and the left precentral gyrus during a Go/No Go task. They also found altered functional connectivity patterns in children with ADHD during the task. Data from the fNRI scans was also used to create machine learning classifiers, which could successfully distinguish between children with ADHD and the controls. Challenges in applying biomarkers in clinical practice The World Federation for ADHD and the World Federation of Societies of Biological Psychiatry have identified criteria that candidate biomarkers must meet before they can be used in the clinic. For a biomarker to be clinically relevant, it should have specificity and sensitivity of at least 80% and be easy to use in the clinic – that is, non-invasive, reliable, simple to use and cost effective. It should also be confirmed by two independent studies that are published in peerreviewed journals. In accordance with these guidelines, there has yet to be a clinically valid biomarker identified for ADHD, in part due to the vast differences in how ADHD may present in an individual. In a recent review, Cortese and colleagues outlined how “methodological limitations, including small sample size, lack of standardization, confounding factors and poor replicability” have slowed progress in the field of ADHD biomarkers. They propose that larger and more robust studies supported by increased international collaboration could improve diagnostic accuracy of biomarkers and pave the way to their use in the clinic. With the varied disciplines of research into potential ADHD biomarkers, there could be the potential to combine datasets to improve understanding of the mechanisms behind ADHD and identify clinically relevant biomarkers in future. 22 BIOMARKERS IN FOCUS The Technique That Can Distinguish Up-Market Floral Honey Leo Bear-McGuinness All honey is made by bees, but that doesn’t mean it’s all uniform. To produce the golden syrup, bees gather pollen from countless different species of flower. As such, most honey sold around the world can be considered poly-floral. Some up-market producers, however, claim their honey is mono-floral – made from pollen predominantly sourced from the same species of flower. Mānuka honey, made from the pollen of the New Zealand mānuka tree, is one of the more notable varieties sold in this high-end market, along with honey made from Scottish heather, buckwheat, clover and blueberry plants. But how can consumers know their expensive jar of honey is actually pure? After all, the honey market has become notorious in recent years for defrauding customers. According to the European Commission, just under half of the commercial honey sold on the continent is fraudulent. In a report published in 2023, the governing body found that out of 320 products sourced from 20 countries, 147 (46%) contained suspicious adulterants such as syrup made from rice and wheat. Is the high-end world of honey really above such fraud? Fortunately, one research team has developed a test to help determine just that. Credit: iStock/Deniz Cengiz TECHNOLOGYNETWORKS.COM BIOMARKERS IN FOCUS 23 Floral honey testing “The aim of my study was to develop a method based on liquid chromatography [LC] coupled to HRMS [highresolution mass spectrometry] for the classification of honeys from different floral origin, including buckwheat, clover and blueberry,” said Dr. Lei Tian, a researcher at McGill University’s Department of Food Science and Agricultural Chemistry. Speaking at Technology Networks’ Advances in Food & Beverage Analysis 2022, Tian began her presentation by busting a few myths about mono-floral honey. “Mono-floral honey is a type of honey, which has a distinctive flavor or other attribute due to its being predominantly from the nectar of a single plant species,” she said. “But we can never control a bee, [make] it focus on one flower or one type of flower. This is very difficult. In fact, [it’s rare] to have 100% mono-floral honey. According to the literature, if one pollen type is representative of more than 45% of the total number of pollen in the sample, it [can be] identified as a monofloral honey.” With that lower-than-expected threshold in mind, Tian sourced 45 honey products, advertised as mono-floral, from a market in Montreal to use as samples for her testing method. “I selected the mono-floral honey: blueberry buckwheat and clover,” she told the Technology Networks audience. “In total, we collected 45 honey samples, 15 for each type, and we used 30 samples to build the classification model, 10 for each type.” Once happy with the honey, the next step was preparing the samples for floral honey testing. “For the method to analyze the honeys,” Tian continued, “we use a dilution and injection method. It's very simple. We take 0.2 grams of honey and we dilute with acetonitrile and water mixture, [in a] one to one ratio, and then we filter the solution through the 0.22 micrometer filter. And then, for the filtrate, we further dilute it 10 times using water. And then the final solution we inject directly into the LC-QTOF-MS [liquid chromatography coupled to quadrupole time-of-flight mass spectrometry].” Finally, once the samples were tested by the “diluteand- shoot” method, Tian and her colleagues validated their results. “For data treatment and interpretation for the honey classification, we acquire the data under different LCMS conditions,” she said, “because one of our objectives in the present study was to find the best working condition to study honey.” “And then, for data processing, we have different steps to select the feature list, which is used to build the models for the honey botanical classification. For data analysis, we build the classification model with different samples selected. And then, for data interpretation, we classify the honey and check the impact of the different LC-MS conditions on the data quality we acquired.” The bees’ tease Honey is one of the most counterfeited foods on the market. A substantial portion of these adulterated products comes from China, according to the Honey Authenticity Network. Given that country-of-origin labeling is not required by most regulators for a product blended from elements sourced from more than one country, many shoppers will be unaware that their honey is Chinese and potentially less pure than advertised. In her study, published in Analytica Chimica Acta in 2024, Tian reports that her “dilute-and-shoot” method successfully distinguished all the buckwheat, clover and blueberry honeys. Indeed, the total ion chromatograms (TICs) recorded were so precise that the team observed an additional signifier in the blueberry honey: a unique TIC peak with a retention time of 2.88 minutes. With its efficacy now proven, Tian says her “relatively fast, simple method” is ready to be further improved by other researchers and analysts within the food sector, TECHNOLOGYNETWORKS.COM BIOMARKERS IN FOCUS 24 for the future development of “advanced predictive models for honey botanical origin.” Before long, the method could be central to any new floral honey testing required by regulators. Until then, consumers of blueberry honey will just have to have a little faith in their brand of choice. ABOUT THE INTERVIEWEE: Dr. Lei Tian obtained a PhD degree in food science from McGill University in 2020. She then worked as a postdoctoral fellow at the University of Amsterdam, Netherlands, on microplastics analysis. Currently, she is leading a project in the laboratory of Prof. Stéphane Bayen at McGill University, on the development and optimization of a standardized non-targeted analysis approach to track various quality attributes of food simultaneously. Credit: iStock/Boogich 25 BIOMARKERS IN FOCUS What Blood Biomarkers Can – and Can’t – Tell Us About Alzheimer’s Rhianna-lily Smith Efforts to diagnose Alzheimer’s disease earlier and more easily are picking up pace, especially with the rise of blood-based biomarkers. These tests measure proteins such as pTau217 and pTau181 in the blood, and are being explored as simpler, less invasive alternatives to traditional diagnostic methods. These markers mirror the same pathological features found in brain tissue or cerebrospinal fluid (CSF). Researchers hope they could help detect signs of disease many years before symptoms begin, potentially at a stage where therapeutic intervention may slow progression. Historically, Alzheimer’s diagnosis has relied on a combination of clinical assessments and more invasive tests. These include neuroimaging methods such as PET scans to detect amyloid plaques and lumbar puncture procedures to collect CSF, where beta-amyloid and tau levels can be measured. These markers have been used for years to support diagnosis, particularly in research settings and memory clinics. “Protein aggregates of beta-amyloid and of the tau protein in the brain are the key pathological hallmarks that define Alzheimer’s disease,” Dr. Patrick Oeckl, a neuroscientist at the German Center for Neurodegenerative Diseases, told Technology Networks. Oeckl leads a research group focused on translational biomarker development using advanced mass spectrometry techniques, and has over 15 years of experience in the field. Credit: iStock/angelp TECHNOLOGYNETWORKS.COM BIOMARKERS IN FOCUS 26 According to Oeckl, biomarker changes can be detected long before symptoms begin. “Exact numbers are difficult to predict,” he said, “but the current data indicate that changes might be measured as early as 20 years before the clinical diagnosis of Alzheimer’s disease.” While blood tests may be easier to use, the fluid they rely on brings a different set of challenges. This article explores the scientific potential, limitations and future of blood-based biomarkers for Alzheimer’s, and why fluid context still matters. The promise of blood-based Alzheimer’s biomarkers Blood-based biomarkers for Alzheimer’s typically involve measuring abnormal forms of tau or betaamyloid proteins in the bloodstream. Over the past decade, advances in assay sensitivity, particularly for forms such as pTau217 and pTau181, have made it possible to detect these proteins at very low concentrations in plasma. This has raised hopes for routine, non-invasive screening tools that could complement or even replace more invasive procedures. “These biomarkers are very promising and studies indicate that their performance can be identical to the measurement in cerebrospinal fluid,” Oeckl said. Another blood-based biomarker under investigation is beta-synuclein, a protein found at synapses that is released into the bloodstream when these connections begin to deteriorate. A 2025 study led by Oeckl found that blood levels of beta-synuclein began to rise ~11 years before the expected onset of dementia symptoms in individuals with a genetic predisposition to Alzheimer’s. The research followed participants in the Dominantly Inherited Alzheimer’s Network (DIAN), combining biomarker data with cognitive tests and brain scans. While the study focused on familial Alzheimer’s disease, Oeckl noted that similar patterns may apply to sporadic cases. If confirmed, beta-synuclein could be used not just for early diagnosis, but also to monitor treatment effects in trials targeting neurodegeneration. “I am excited to see if synaptic biomarkers such as betasynuclein can be used to monitor treatment effects of novel anti-amyloid and other drugs,” said Oeckl. Fluid-specific performance and diagnostic risk Not all biomarkers perform equally well across different biofluids. CSF, which is in direct contact with the brain and spinal cord, tends to provide a more specific picture of central nervous system pathology. Blood, however, circulates throughout the entire body and contains proteins from multiple organs and tissues, which can interfere with interpretation. “Some biomarkers show equally good performance in CSF and blood,” Oeckl said, “whereas for others, especially those with expression also in non-brain tissue, measurement in CSF is better.” One example is tau, a key Alzheimer’s-related protein. Research has shown that tau proteins, including pTau217 and pTau181, can also be released from muscle tissue in certain diseases. In a recent study, blood levels of these proteins were elevated not only in Alzheimer’s disease patients but also in individuals with amyotrophic lateral sclerosis (ALS), a neurodegenerative muscle disease. “It is not yet completely clear how other diseases of muscle tissue affect blood levels of pTau217 and pTau181,” Oeckl noted. Further investigation using muscle biopsies revealed the presence of these phosphorylated tau proteins in muscle tissue, with ALS samples showing increased TECHNOLOGYNETWORKS.COM BIOMARKERS IN FOCUS 27 pTau reactivity in atrophic muscle fibers. These findings suggest that elevated blood pTau levels might reflect pathology outside the brain, such as muscle degeneration, underscoring the importance of careful interpretation of blood biomarker results. A blood test therefore might show elevated tau levels that are unrelated to Alzheimer’s pathology, pointing to the disease when the signal is coming from somewhere else entirely. This is a key reason why CSF often gives a more accurate picture; it reflects what’s happening in the brain, without interference from the rest of the body. The future of blood-based biomarkers Despite these limitations, blood biomarkers are advancing quickly, and researchers continue to explore their role in diagnostic workflows. “I’m most excited about how and if blood biomarkers such as pTau217 will be integrated in clinical routine,” said Oeckl. “Especially in primary care and for preclinical diagnosis.” One proposed approach is to use these biomarkers in a tiered testing model. Blood tests could be used for broad initial screening, followed by more specific confirmation through CSF analysis or neuroimaging when needed. This could expand access to early detection without relying solely on more invasive or costly methods. The growing promise of blood-based biomarkers is now also beginning to translate into real-world clinical tools. In May 2025, the US Food and Drug Administration (FDA) cleared the first blood test to aid in the diagnosis of Alzheimer’s disease: the Lumipulse G pTau217/β- Amyloid 1-42 Plasma Ratio. Designed for use in adults aged 55 and older with symptoms of cognitive decline, the test measures levels of pTau217 and β-amyloid 1-42 in plasma to infer the presence of amyloid plaques in the brain. While not a stand-alone diagnostic tool, this FDAcleared test provides a less invasive and potentially more accessible alternative to PET imaging or lumbar punctures – representing an important step toward broader clinical implementation of bloodbased biomarkers. ABOUT THE INTERVIEWEE: Dr. Patrick Oeckl is a neuroscientist and head of the research group for Translational Mass Spectrometry and Biomarker Research at the German Center for Neurodegenerative Diseases Ulm (DZNE) and Ulm University Hospital, Department of Neurology, Germany. His interest is to uncover pathophysiological mechanisms of neurodegenerative diseases and the discovery, validation and implementation of biomarkers in CSF and blood to improve diagnosis, personalized medicine and drug development. He has more than 15 years of experience in biomarker discovery and validation in academia and industry using mass spectrometry and immunoassays with a high expertise in the challenging measurement of low abundant proteins by mass spectrometry in biofluids such as synucleins or neurofilaments. He has published more than 100 peer-reviewed articles in scientific journals. BIOMARKERS IN FOCUS 28 TECHNOLOGYNETWORKS.COM CONTRIBUTORS Alexander Beadle Alexander is a Science Writer and Editor for Technology Networks. He writes news and features for the Applied Sciences section, leading the site's coverage of topics relating to materials science and engineering. Before joining Technology Networks in 2023, Alexander worked as a freelance science writer, reporting on a broad range of topics including cannabis science and policy, psychedelic drug research and environmental science. He holds a masters degree in Materials Chemistry from the University of St Andrews, Scotland. Blake Forman Blake pens and edits breaking news, articles and features on a broad range of scientific topics with a focus on drug discovery and biopharma. Blake earned an honors degree in chemistry from the University of Surrey, which involved a placement year at the Medicines and Healthcare products Regulatory Agency (MHRA) laboratory, where he developed new pharmaceutical testing methods. Blake also holds an MSc in chemistry from the University of Southampton. His research project focused on the synthesis of novel fluorescent dyes often used as chemical/bio-sensors and as photosensitizers in photodynamic therapy. Blake held several editorialbased roles before joining Technology Networks as Senior Science Writer in 2024. Isabel Ely, PhD Isabel is a Science Writer and Editor at Technology Networks. She holds a BSc in exercise and sport science from the University of Exeter, a MRes in medicine and health and a PhD in medicine from the University of Nottingham. Her doctoral research explored the role of dietary protein and exercise in optimizing muscle health as we age. Katie Brighton Katie joined Technology Networks in January 2022 after completing a bachelor’s degree in biochemistry and a master’s by research degree in molecular and cellular biology, both at the University of Leeds. They loved the breadth of scientific content covered in their undergraduate studies and wanted to share their passion for research through science communication. As a scientific copywriter, Katie assembles newsletters, writes promotional webinar copy, supports the publication’s inhouse writers and produces scientific content. Leo Bear-McGuinness Leo is a Science Writer at Technology Networks where he focuses on environmental and food research. He holds a bachelor's degree in biology from Newcastle University and a master's degree in science communication from the University of Edinburgh. Molly Coddington Molly is a Senior Writer and Newsroom Team Lead at Technology Networks. Molly reports on various scientific topics, covering the latest breaking news and writing long-form pieces. Before joining Technology Networks in 2019, Molly worked as a clinical research associate in the NHS and as a freelance science writer. She has a firstclass honors degree in neuroscience from the University of Leeds and received a Partnership Award for her efforts in science communication. Rhianna-lily Smith Rhianna-lily graduated from the University of East Anglia with a BSc in biomedicine and completed her MSc by Research in microbiology at the Quadram Institute Bioscience in 2023. Her research primarily focused on the gut microbiome in pregnant women throughout gestation. During her MSc, she developed a passion for science communication and later joined Technology Networks as an Editorial Assistant.