In order to bridge the gap between clinical outcome and the underlying predictive molecular and physiological mechanisms, medicine relies on precise and powerful indicators called biomarkers. These measurable molecules or biological characteristics allow for earlier diagnosis, more accurate monitoring of disease progression and responsiveness to the therapeutic interventions thus providing the basis for personalised treatment strategies. Despite their transformative impact on diagnostics, prognostics and drug development, challenges remain regarding their specificity, sensitivity, and standardisation across different settings. How can we fully discover the potential of biomarkers and revolutionise patient care? We’ll find out in this article, along with the unique use of biomarkers in the Neuromind device.
Biomarkers decoded: Unraveling nature’s signals
A biomarker, short for biological marker, is any measurable biological parameter that can be used to assess health or disease states. According to the U.S. National Institutes of Health (NIH), a biomarker is defined as: “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.” [1]
Simply put, biomarkers are like signposts that provide clues about what’s happening inside our bodies.
A biological marker can refer to any measurable indicator derived from biological processes. For example, biomarkers may be observed at both cellular and molecular levels (including DNA, RNA, proteins and metabolites).
Biomarkers are obtained from either tissue samples or liquid biopsies (blood, urine, saliva, etc.). Additionally, other types of biomarkers—such as those reflecting physiological or morphological characteristics—can be captured using clinical or medical imaging techniques such as electroencephalography (EEG) or functional magnetic resonance imaging (fMRI).
Biomarkers can be categorized as either quantitative or qualitative:
– qualitative biomarkers are generally used in binary analyses to determine the presence or absence of a pathological process;
– quantitative biomarkers help identify a disease process once a specific threshold is exceeded.
Classification of biomarkers: The spectrum of signaling
Biomarkers can be classified based on their applications and the information they provide. [2][3][4]
Diagnostic biomarkers
The diagnostic biomarkers help in the identification of a disease or condition at an early stage. For instance, elevated levels of prostate-specific antigen (PSA) can serve as an early indicator of prostate cancer.
Prognostic biomarkers
Prognostic biomarkers provide information about the likely course or outcome of a disease. For example, certain gene expression profiles in tumors can predict the aggressiveness of cancer and guide treatment decisions.
Predictive biomarkers
Predictive biomarkers are used to identify the likelihood of a patient’s response to a specific treatment. For example, the presence of the HER2 protein in breast cancer cells predicts a better response to trastuzumab, a targeted therapy.
Pharmacodynamic biomarkers
Pharmacodynamic biomarkers indicate the biological response to a therapeutic intervention in real time. For example, the Bispectral Index (BIS), a derived EEG parameter, is used to monitor the level of consciousness during surgery.
Surrogate endpoints
In clinical research, surrogate endpoints are biomarkers intended to substitute for a clinical endpoint. They can provide earlier insights into treatment effects and are particularly useful when the true clinical endpoints require long-term observation.
Examples of physiology-based biomarkers
Electrodermal Activity (EDA)
EDA measures skin conductance, providing an indication of autonomic arousal and stress responses. This marker is widely used in affective neuroscience to gauge emotional reactivity.
For example, one study found that fluctuations in EDA correlate closely with neural responses to uncertainty and arousal during anticipation. [5]
Electrocardiography (ECG)
Electrocardiography offers a direct view into the heart’s electrical activity and is crucial for monitoring cardiovascular health. ECG can reflect both the intrinsic cardiac function and physiological responses to stress, making it a valuable tool for real-time cardiovascular assessment.
For example, research using wearable ECG systems has shown that continuous monitoring can detect subtle changes in heart rhythm. [6]
Heart Rate Variability (HRV)
Heart Rate Variability measures the variation in time intervals between consecutive heartbeats, serving as a non-invasive indicator of autonomic nervous system balance. HRV is a reliable biomarker for understanding physiological dynamics and can be instrumental in both clinical diagnostics and wellness monitoring. [7]
The Task Force of the European Society of Cardiology (1996) has established HRV as a reliable measure for assessing cardiovascular and emotional responses. [8]
Electroencephalography (EEG)
EEG records the brain’s electrical activity, which is crucial for assessing different cognitive and emotional states. Its ability to capture dynamic changes in neural function allows for detailed monitoring of consciousness and mental health.
For example, EEG-derived biomarkers have been shown to detect early signs of dementia and to help characterize neurodevelopmental disorders, underlining their importance in clinical diagnostics. [9][10]
Photoplethysmography (PPG)
PPG is a non-invasive optical technique used to measure blood volume changes at the skin surface, thereby providing insights into cardiovascular dynamics.
For example, PPG-derived parameters have been effectively applied to evaluate vascular age and monitor hemodynamic function in clinical environments. [11][12]
Accelerometer Data
Accelerometers measure physical motion and activity patterns, making them useful for assessing overall activity levels and detecting movement abnormalities. This objective data is relevant for monitoring motor function in various settings.
For example, movement features extracted from accelerometer data have been used as predictive biomarkers for muscle atrophy in neurocritical care settings. [13]
Breath Rhythms
Analysis of respiratory patterns provides crucial information about autonomic balance and stress responses. Although still emerging, this physiology-based biomarker holds promise for improving our understanding of emotional regulation through breathing metrics.
For example, variations in respiratory sinus arrhythmia have been linked to differences in emotion regulation and depressive symptoms in children. [14]
Eye Tracking
Eye-tracking technology offers valuable insights into visual attention and cognitive processing. The subtle shifts help determine where and how individuals focus their visual resources.
For example, eye-tracking metrics have been used as biomarkers to assess treatment effects in young children with autism, demonstrating their potential in evaluating cognitive and behavioral changes. [15]
Applications of biomarkers in research and personalised medicine
The utility of biomarkers extends far beyond diagnosis. In drug development, biomarkers are used to:
– enhance clinical trial design: by identifying patients who are most likely to respond to a new therapy, biomarkers can help stratify patient populations and improve the efficiency of clinical trials;
– monitor drug efficacy and safety: biomarkers provide a means to monitor the biological effects of a drug, thereby offering early indications of efficacy and potential side effects;
– guide personalised treatment: in the era of personalised medicine, biomarkers enable healthcare providers to tailor treatments to the genetic and molecular profiles of individual patients, improving outcomes and minimising adverse effects.
In research settings, biomarkers are essential for understanding disease mechanisms. They offer insights into the molecular pathways that drive disease progression and help identify potential therapeutic targets.
For example, the identification of specific genetic biomarkers in cancer has led to the development of targeted therapies that inhibit key molecular pathways involved in tumor growth.
Challenges in biomarker development
Despite their potential, the development and implementation of biomarkers face several challenges. [4]
Specificity and sensitivity
A biomarker must be both highly specific to a particular disease and sensitive enough to detect early or subtle changes. However, many biomarkers can be influenced by multiple factors, which can compromise their diagnostic accuracy.
Standardisation
Variability in sample collection, processing, and analysis can lead to inconsistent results. Developing standardised protocols is key for ensuring that biomarker measurements are reliable and reproducible across different laboratories and clinical settings.
Regulatory hurdles
Before being adopted in clinical practice, biomarkers must undergo rigorous validation and regulatory approval. This process can be time-consuming and expensive, potentially delaying the transition of promising biomarkers from research to clinical use.
Biological complexity
Diseases often involve complex interactions among various biological pathways. A single biomarker may not capture the full picture of a disease process. Therefore, using biomarker panels or a combination of markers is sometimes necessary to achieve accurate diagnosis and prognosis.
Neuromind: A unique device combining proprietary attention & emotion biomarkers
Neuromind is an innovative neurofeedback solution that combines advanced sensor technologies with immersive virtual reality to measure and modulate emotional states.
Developed by a research team combining experts in neuroscience and data science, in collaboration with a scientific committee of the renowned experts, our device emerged from a psychophysiological study at the Paris Brain Institute. We collected electrophysiological data from both the central and peripheral nervous systems to identify new emotional biomarkers.
Using machine learning algorithms, Neuromind translates brain activity into two key emotional parameters:
– arousal (reflecting energy or calmness);
– valence (indicating the positivity or negativity of an emotion).
These biomarkers allow the system to map a user’s emotional state in real time, creating a clear picture of their current mood. This information is then used in a closed-loop system where the virtual reality environment adapts dynamically to guide the user toward a desired state, such as relaxation or mindfulness.
This adaptive immersion not only supports therapeutic interventions for conditions like depression but also provides a valuable tool for research and treatment optimisation. Recognised for its potential, Neuromind has garnered awards and industry support, marking a significant step forward in the field of digital health and neuroscience.
Looking ahead, biomarkers will continue to play an essential role in the advancement of personalised medicine. Their ability to provide real-time insights holds promise for more targeted and effective therapies. Innovative devices such as Neuromind are already pushing these boundaries by integrating electrophysiological monitoring with adaptive virtual reality to fine-tune emotional states and enhance therapeutic outcomes. This cutting-edge solution not only exemplifies the practical application of biomarkers but also paves the way for improved patient care in mental health and beyond. If you’d like to find out how our device works, we’d be delighted to arrange a demonstration.
References
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[2] U.S. Department of Health and Human Services. Biomarkers Toolkit.
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[10] Goodspeed K, Armstrong D, Dolce A, Evans P, Said R, Tsai P, Sirsi D. Electroencephalographic (EEG) Biomarkers in Genetic Neurodevelopmental Disorders. J Child Neurol. 2023 May;38(6-7):466-477. doi: 10.1177/08830738231177386. Epub 2023 Jun 1. PMID: 37264615; PMCID: PMC10644693.
[11] Charlton PH, Paliakaitė B, Pilt K, Bachler M, Zanelli S, Kulin D, Allen J, Hallab M, Bianchini E, Mayer CC, Terentes-Printzios D, Dittrich V, Hametner B, Veerasingam D, Žikić D, Marozas V. Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: a review from VascAgeNet. Am J Physiol Heart Circ Physiol. 2022 Apr 1;322(4):H493-H522. doi: 10.1152/ajpheart.00392.2021. Epub 2021 Dec 24. PMID: 34951543; PMCID: PMC8917928.
[12] Almarshad MA, Islam MS, Al-Ahmadi S, BaHammam AS. Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review. Healthcare (Basel). 2022 Mar 16;10(3):547. doi: 10.3390/healthcare10030547. PMID: 35327025; PMCID: PMC8950880.
[13] Schmidbauer ML, Putz T, Gehri L, Ratkovic L, Maskos A, Zibold J, Bauchmüller J, Imhof S, Weig T, Wuehr M, Dimitriadis K. Accelerometer-derived movement features as predictive biomarkers for muscle atrophy in neurocritical care: a prospective cohort study. Crit Care. 2024 Aug 31;28(1):288. doi: 10.1186/s13054-024-05067-y. PMID: 39217360; PMCID: PMC11366141.
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