Biomarker integration and biosensor technologies enabling AI-driven insights into biological aging

Area of Science:

• Biochemistry
• Gerontology
• Biotechnology

Background:

• The global population is aging, increasing the need for accurate biological age assessment.
• Biological age, reflecting physiological state, is a better health-span indicator than chronological age.
• Key biochemical markers like CRP, IGF-1, IL-6, and GDF-15 are crucial for aging research.

Purpose of the Study:

• To review the role of Artificial Intelligence (AI) and biosensor technologies in measuring and interpreting aging biomarkers.
• To explore how AI enhances the analysis of complex biological data for aging.
• To discuss the potential of AI-driven tools for personalized health monitoring and disease risk assessment.

Main Methods:

• Review of current literature on AI, biosensors, and biochemical markers of aging.
• Analysis of AI techniques including machine learning, deep learning, and generative models.
• Exploration of biosensor applications in biochemical marker detection.

Main Results:

• AI and biosensors significantly enhance the measurement and interpretation of aging biomarkers (CRP, IGF-1, IL-6, GDF-15).
• AI facilitates the analysis of high-dimensional datasets, leading to data-informed health monitoring tools.
• These technologies support personalized health trajectories, risk profiles, and treatment response assessment.

Conclusions:

• AI and biosensor integration offers a pathway to more personalized and accessible precision aging frameworks.
• Data-driven insights from these technologies can revolutionize health monitoring and disease prevention.
• Further research is needed to address challenges and optimize AI implementation in aging research.