Converging Paths: Artificial Intelligence for Integrating Biological and Digital Biomarkers in Personalized Longevity Medicine
- 1Institute for Fundamental and Applied Medical Research, S.D. Asfendiyarov Kazakh National Medical University, Tole Bi Street 94, Almaty 050000, Kazakhstan
- 2Department of Biomedical Sciences, School of Medicine, Nazarbayev University, Astana 010000, Kazakhstan
Abstract
Ageing is a multidimensional biological process characterised by interacting molecular, physiological, functional, and environmental changes that chronological age alone cannot adequately represent. The rapid development of epigenetic clocks, proteomic and metabolomic signatures, imaging-derived biomarkers, wearable sensors, and digital phenotypes has created an increasingly rich but fragmented landscape of aging biomarkers. Artificial intelligence (AI) offers a potential integrative framework that can combine these heterogeneous data streams and convert them into multidimensional estimates of biological age, health risk, functional reserve, and potentially treatment response. Recent studies demonstrate that molecular and clinical information can be integrated into composite aging measures, while independent work shows that wearable movement data, retinal imaging, and speech characteristics can provide non-invasive digital representations of aging-related phenotypes. The emerging concept of multimodal aging clocks extends these approaches by integrating physiological, clinical, imaging, and molecular measurements into a common representation of biological aging.
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BibTeX
@article{saliev2026converging,
title = {{Converging Paths: Artificial Intelligence for Integrating Biological and Digital Biomarkers in Personalized Longevity Medicine}},
author = {Timur Saliev and Prim B. Singh},
journal = {International Journal of Molecular Sciences},
year = {2026},
month = oct,
doi = {10.3390/ijms27219458},
url = {https://www.mdpi.com/1422-0067/27/21/9458},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Converging Paths: Artificial Intelligence for Integrating Biological and Digital Biomarkers in Personalized Longevity Medicine
AU - Timur Saliev
AU - Prim B. Singh
PY - 2026
DA - 2026/10/23
JO - International Journal of Molecular Sciences
DO - 10.3390/ijms27219458
UR - https://www.mdpi.com/1422-0067/27/21/9458
AB - Ageing is a multidimensional biological process characterised by interacting molecular, physiological, functional, and environmental changes that chronological age alone cannot adequately represent. The rapid development of epigenetic clocks, proteomic and metabolomic signatures, imaging-derived biomarkers, wearable sensors, and digital phenotypes has created an increasingly rich but fragmented landscape of aging biomarkers. Artificial intelligence (AI) offers a potential integrative framework that can combine these heterogeneous data streams and convert them into multidimensional estimates of biological age, health risk, functional reserve, and potentially treatment response. Recent studies demonstrate that molecular and clinical information can be integrated into composite aging measures, while independent work shows that wearable movement data, retinal imaging, and speech characteristics can provide non-invasive digital representations of aging-related phenotypes. The emerging concept of multimodal aging clocks extends these approaches by integrating physiological, clinical, imaging, and molecular measurements into a common representation of biological aging.
SN - 1422-0067
ER -