Biomarker integration and biosensor technologies enabling AI-driven insights into biological aging.
Kushner. Jared A JA; Pandey. Mohit M; Kohli. Sandeep Sonny S SSS
Key Findings
- IGF‑1 is highlighted as one of the main blood markers that reflect biological aging.
- AI methods (machine learning, deep learning) can better interpret complex IGF‑1 data together with other markers.
- Biosensor and wearable technologies are being developed to allow easier, possibly at‑home, measurement of IGF‑1 and related biomarkers.
- Implementation challenges remain, such as data privacy, sensor accuracy, and integration into everyday health monitoring.
Practical Outcomes
- For biohackers, the takeaway is that emerging AI‑driven tools could soon let you track IGF‑1 levels more easily and get personalized insights about aging. While the review doesn’t change how you should dose IGF‑1, it suggests keeping an eye on new home‑testing kits and AI platforms that integrate IGF‑1 with other health markers for more informed longevity strategies.
Summary
The paper talks about using new sensor devices and AI to measure and understand four aging‑related chemicals in the blood, including IGF‑1. It says that smarter analysis can give a clearer picture of how fast you’re really aging and help spot health risks earlier, but it doesn’t give any new dosing or treatment tips.
Abstract
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. This review focuses on four key biochemical markers - C-Reactive Protein (CRP), Insulin like Growth Factor-1 (IGF-1), Interleukin-6 (IL-6), and Growth Differentiation Factor-15 (GDF-15) - and explores how Artificial Intelligence (AI) and biosensor technologies enhance their measurement and interpretation. AI-driven methods including machine learning, deep learning, and generative models 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 AI-driven methods in precision aging frameworks.
Study Information
pubmed
2025
2025-11-07T00:00:00.000Z
10.3389/fragi.2025.1703698
147