Biomarker Integration and Biosensor Technologies Enabling AI-Driven Insights into Biological Aging
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| Format: | Preprint |
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2025
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| author | Kushner, Jared A Pandey, Mohit Sandeep Kohli, S |
| author_facet | Kushner, Jared A Pandey, Mohit Sandeep Kohli, S |
| contents | As the global population continues to age, there is an increasing demand for ways to accurately quantify the biological processes underlying aging. Biological age, unlike chronological age, reflects an individual's physiological state, offering a more accurate measure of health-span and age-related decline. Aging is a complex, multisystem process involving molecular, cellular, and environmental factors and can be quantified using various biophysical and biochemical markers. This review focuses on four key biochemical markers that have recently been identified by experts as important outcome measures in longevity-promoting interventions: C-Reactive Protein, Insulin like Growth Factor-1, Interleukin-6, and Growth Differentiation Factor-15. With the use of Artificial Intelligence, the analysis and integration of these biomarkers can be significantly enhanced, enabling the identification of complex binding patterns and improving predictive accuracy for biological age estimation and age-related disease risk stratification. Artificial intelligence-driven methods including machine learning, deep learning, and multimodal data integration, facilitate the interpretation of high dimensional datasets and support the development of widely accessible, data-informed tools for health monitoring and disease risk assessment. This paves the way for a future medical system, enabling more personalized and accessible care, offering deeper, data-driven insights into individual health trajectories, risk profiles, and treatment response. The review additionally highlights the key challenges, and future directions for the implementation of artificial intelligence-driven methods in precision aging frameworks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_20150 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Biomarker Integration and Biosensor Technologies Enabling AI-Driven Insights into Biological Aging Kushner, Jared A Pandey, Mohit Sandeep Kohli, S Quantitative Methods As the global population continues to age, there is an increasing demand for ways to accurately quantify the biological processes underlying aging. Biological age, unlike chronological age, reflects an individual's physiological state, offering a more accurate measure of health-span and age-related decline. Aging is a complex, multisystem process involving molecular, cellular, and environmental factors and can be quantified using various biophysical and biochemical markers. This review focuses on four key biochemical markers that have recently been identified by experts as important outcome measures in longevity-promoting interventions: C-Reactive Protein, Insulin like Growth Factor-1, Interleukin-6, and Growth Differentiation Factor-15. With the use of Artificial Intelligence, the analysis and integration of these biomarkers can be significantly enhanced, enabling the identification of complex binding patterns and improving predictive accuracy for biological age estimation and age-related disease risk stratification. Artificial intelligence-driven methods including machine learning, deep learning, and multimodal data integration, facilitate the interpretation of high dimensional datasets and support the development of widely accessible, data-informed tools for health monitoring and disease risk assessment. This paves the way for a future medical system, enabling more personalized and accessible care, offering deeper, data-driven insights into individual health trajectories, risk profiles, and treatment response. The review additionally highlights the key challenges, and future directions for the implementation of artificial intelligence-driven methods in precision aging frameworks. |
| title | Biomarker Integration and Biosensor Technologies Enabling AI-Driven Insights into Biological Aging |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2508.20150 |