AI Driven Biomarkers Could Help Prevent Age-Related Diseases

Biomarkers are used to assess biological age which is indicative of a person’s state of health. Reliable Biomarkers of Longevity integrated with AI and machine learning techniques have the potential to help prevent age-related diseases and extend health span.

Biomarkers are measurable indicators of a biological state and are the primary metric in P4 medicine. They are used to measure the presence of disease and to help determine a prognosis. They are used in numerous kinds of research, both in vitro and in vivo, and almost always include human studies. The process involves continuously monitoring a person and recommending a series of corrective interventions. The ongoing monitoring of small changes in health, and micro-adjustments to treatment, requires a panel of biomarkers. It will be impossible to make progress in biotechnology and preventive medicine without such biomarkers. 

Although individual biomarkers are difficult to classify, scientists identify three specific types: exposure biomarkers, effect biomarkers, and susceptibility biomarkers. These can be used as indicators for several purposes. For example, biomarkers can confirm disease risk in an individual, reveal an individual’s sensitivity to specific treatments, and inform recommendations for future treatments. A single class of Longevity Biomarkers have been used to predict biological age. DNA methylation was used to predict age with an error of about 3.6 years using 8,000 samples, and 3D facial images have also been used to predict age with a mean deviation of 6 years. Integration of multiple biomarkers could be much more powerful.

AI enables the analysis of cross-sectional and longitudinal data related to large human populations. An enormous amount of health information is already digitized, which allows us to analyze data and compare patient information. Advances in the development of biomarkers will allow doctors to assess health, quantify the effect of interventions, and produce personalized medical reports. AI allows for enhanced precision in creating panels of actionable biomarkers, enables rapid assessment, and facilitates preventive measures.

Digital Biomarkers

Thanks to advances in digital technology we now have access to a whole new form of measurable indicator: digital biomarkers. Digital biomarkers are like other biomarkers but they are measured using gadgets. Objective, quantifiable, physiological data is collected and measured by means of digital devices that are portable, wearable, implantable, or digestible. This data can be used to confirm the presence of disease and potentially prevent pathologies. Digital biomarkers could revolutionize the way doctors monitor patients and predict disease outcomes.

Digital biomarkers can be used in the development of unique sensing platforms and chips, human liquid testing systems, devices for health monitoring, and single-cell profiling devices. They provide unique information that fuels development of new diagnostics, vaccines, and therapeutics. All of these enable novel approaches for preventing and treating many diseases. Digital biomarkers could potentially reorganize the whole Pharma industry and become an integral part of the drug development process. Due to the sensitivity and precision they provide, digital biomarkers could be used to improve clinical trials where they could be used to determine appropriate dosage, monitor side effects, and reveal drug efficacy for individual patients.

Biomarkers of Longevity

There is no single gold standard biomarker to monitor healthy longevity. The current approach is to use biomarkers from patients who are being treated for a specific diseases in the hospital. However, developing Biomarkers of Longevity requires collecting vast amounts of data from healthy people who have no trace of disease. Ideally, this data should be collected from healthy people who are biologically thirty-five and who are not in the hospital. There are options available for aggregating data from such demographics. One option is to use AI for the development of an optimal panel. Although this is one of the most important diagnostic services that could be offered, it has not received the attention it deserves compared to the tangible benefits it could deliver. 

The biotechnologies necessary for the implementation of P4 Medicine are already available. Now we need to apply AI and big data analytics to develop optimal panels and to determine how to optimize their implementation. This is not merely a biotechnology problem. It’s a data mining, analysis, and management problem. By applying AI we can accelerate the clinical translation of biomarkers toward diagnostics, prognostics, and therapeutics. An MVP panel of biomarkers will allow for rapid assessment, which in turn will allow for experiments with microdoses of different drugs.

Mass health data aggregation based on Biomarkers of Longevity obtained from whole populations will enable a new age of precision diagnostics and prognostics. When the number of biomarkers increases to the thousands, AI will be used to develop an optimal panel, analyze biomarkers from individual patients, and orchestrate therapeutic interventions in response to fluctuations in those biomarkers. 

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