Most of a human’s health is not tracked, being lived outside of clinical observation. This limits both the subject’s and the clinician’s ability to determine the subject’s health status.
In order to optimise an individual’s health, it is important to consider all of the factors that may influence their well-being, not just those that are observed or tracked in a clinical setting. This can involve adopting a holistic approach to healthcare that takes into account an individual’s unique circumstances and needs .
Recent advancements in biotechnology have successfully addressed a range of shortcomings in healthcare, tightening the gap between individuals and optimal health. Our understanding of biomarkers, in addition to improved immunohistochemical techniques, has crucially helped healthcare professionals to conclude accurate diagnoses on their patients . Utilisation of medical devices – such as MRI and ECG scanners – further benefits reliable diagnosis.
Nevertheless, today’s medical imaging capabilities are the relative equivalent of primitive early 20th Century photography, merely providing limited insights on a subject’s health status. It is therefore an opportune moment in history for the intensification of efforts towards innovative, comprehensive medical screening.
Biomarkers comprise a range of objective indicators, categorised as either biomarkers of exposure (guiding risk prediction), and biomarkers of disease (for diagnostic screening and disease progression monitoring) . Innovative companies are sporadically enabling customers to measure the former, evaluating the exposure to certain lifestyle factors.
One commercial example is Vivoo App: an “at-home urine test for personalised nutrition and lifestyle advice with wearable integration”. Nine biomarkers are measured, ranging from magnesium to free radicals . Another pioneer in personalised, biomarker-reliant insights is InsideTracker: a comprehensive technology utilising blood and DNA test data – as well as self-reported lifestyle factors . Most outstandingly, companies such as QBio are pushing the frontiers of data-driven health monitoring .
Yet, as of 2022, the population at large predominantly benefits from biomarkers of disease’s evaluation – which is usually too little, too late. They often overlap between one disease to another, variably associated with different clinical outcomes of interest. Creating biomarker panels is necessary for precision diagnosis, and subsequently precision medicine . They effectively illustrate different pathophysiological processes of a disease, by indicating unique combinations of biomarker groups for specific conditions . Benchmarking biomarkers and biomarker panels by their ratio of accuracy versus actionability, instead of merely their accuracy, is especially important.
From a practical perspective, standard histopathological workflows have limited scope for providing valuable general health insights, apart from specific tissues selectively analysed.
Automated, High-Throughput, Multiplexed, Immunofluorescence Imaging for Rapid I/O Results. Adapted from .
Current standard histopathological workflows are often limited to the analysis of small samples, which can be inadequate for understanding the full extent of a disease or condition . For example, when examining a tissue sample, only a small portion of the tissue can be analysed due to the constraints of the sample size. Additionally, standard histopathological workflows are often restricted to the analysis of static, fixed samples, which can limit their ability to capture the dynamic nature of many diseases or conditions . For example, diseases such as cancer are characterised by rapid cell proliferation and tissue remodelling, which can be difficult to capture using static samples. This can lead to a limited view of the disease process and can result in incomplete or inaccurate diagnoses.
Therefore, advancements in comprehensive medical imaging technologies – following QBio’s futuristic approach – deserve prioritisation amongst stakeholders. Visual examination of biomarkers and their association with phenotypes ought to be considered as important as for their relationship with clinical outcomes. Improved interoperability is one promising aspect, making it easier for technologies such as Q Bio Gemini platform to integrate with other healthcare technologies and systems. That could help to improve the overall effectiveness of the platform and make it more useful to healthcare providers.
Q Bio Gemini, the first clinical, whole-body Digital Twin platform, powered by the Q Bio Mark I autonomous, whole body scanner. Adapted from .
All roads lead to P4 medicine – preventive, predictive, personalised and participatory – which would be significantly reliant on analysing a wide array of biomarkers (some of which are yet to be discovered / acknowledged) . Not only would this result in significant rates of disease prevention, it would also optimise individual (and therefore population) health. A paradigm shift in modern medicine away from single disease treatment toward personalized multi-disease prevention to ameliorate the forms of damage underlying the ageing process. This way, healthspan can be maximised at population-wide scales, resulting in the prevention of lifestyle and age-related diseases from occurring, as well as optimal management of genetic conditions.
Conclusively, it is essential to have effective methods of visualising and interpreting biomarker data. Innovation in this area is therefore crucial, as it allows healthcare providers and researchers to more accurately analyse and understand the factors that may influence an individual’s health. By visualising biomarkers in innovative and intuitive ways, it becomes possible to identify trends and patterns that may not be immediately apparent from raw data alone. This can ultimately lead to more effective prevention and management strategies, and a greater overall improvement in population health.
The importance of innovation in biomarker visualisation cannot be overstated. It is a vital component of P4 medicine and is essential for realising the full potential of this approach to optimize individual and population health.
© COPYRIGHT: This article is the property of We Speak Science, a non-profit organization, co–founded by Dr. Detina Zalli and Dr. Argita Zalli. The article is written by Natanael Strugaj.
- Jasemi, M., Valizadeh, L., Zamanzadeh, V., & Keogh, B. (2017). A Concept Analysis of Holistic Care by Hybrid Model. Indian journal of palliative care, 23(1), 71–80.
- Kim, S. W., Roh, J., & Park, C. S. (2016). Immunohistochemistry for Pathologists: Protocols, Pitfalls, and Tips. Journal of pathology and translational medicine, 50(6), 411–418.
- Mayeux R. (2004). Biomarkers: potential uses and limitations. NeuroRx : the journal of the American Society for Experimental NeuroTherapeutics, 1(2), 182–188.
- Zhou, Q., Yin, J., Wang, Y., Zhuang, X., He, Z., Chen, Z., & Yang, X. (2021). MicroRNAs as potential biomarkers for the diagnosis of Traumatic Brain Injury: A systematic review and meta-analysis. International journal of medical sciences, 18(1), 128–136.
- Jickling, G. C., & Sharp, F. R. (2015). Biomarker panels in ischemic stroke. Stroke, 46(3), 915–920.
- Nayak B. K. (2010). Understanding the relevance of sample size calculation. Indian journal of ophthalmology, 58(6), 469–470.
- Aeffner, F., Zarella, M. D., Buchbinder, N., Bui, M. M., Goodman, M. R., Hartman, D. J., Lujan, G. M., Molani, M. A., Parwani, A. V., Lillard, K., Turner, O. C., Vemuri, V. N. P., Yuil-Valdes, A. G., & Bowman, D. (2019). Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association. Journal of pathology informatics, 10, 9.
- Alonso, S. G., de la Torre Díez, I., & Zapiraín, B. G. (2019). Predictive, Personalized, Preventive and Participatory (4P) Medicine Applied to Telemedicine and eHealth in the Literature. Journal of medical systems, 43(5), 140.