In today's rapidly advancing technological world, AI is not only changing our lifestyles but also quietly opening up new possibilities for human health and longevity.

Recently, Professor Lv Yuxuan from the Institute of Frontier Biotechnology at Southern University of Science and Technology, along with many internationally renowned professors and biotechnology companies, jointly revealed the enormous potential of AI in longevity technology. The publication of this cross-disciplinary review undoubtedly brings new hope to those pursuing a high-quality, long life.

The rapid development of AI technology has brought revolutionary changes to new drug development. Traditional new drugs often take years, even decades, to go from discovery to application, with extremely low success rates. However, the involvement of AI has greatly shortened this process. Through deep learning and big data analysis, AI can quickly screen out potentially effective drugs and even discover new applications for "old drugs." For example, the antihypertensive drug rilmenidine was found by AI to be able to mimic caloric restriction and extend the healthy lifespan of nematodes, adding a new member to the research of anti-aging drugs.

In addition to drug development, AI also plays an important role in the screening and research of aging biomarkers. For example, deep learning algorithms can process cell images from different tissues, identify the morphological characteristics of cell nuclei, and thus use them as a new aging biomarker. This discovery provides new perspectives and methods for aging research, helping us to better understand the mechanisms of aging.

•Creating an "aging clock": The perfect fusion of big data and AI

In aging research, the "aging clock" is an important tool. It can predict an individual's physiological age and life expectancy based on their biomarker data. Big data provides a rich data foundation, while AI optimizes the model through machine learning algorithms, greatly improving the accuracy and reliability of predictions. For example, "OMICmAge" is a high-tech clock that combines multi-omics data and algorithm analysis, which is more accurate than single-omics clocks in predicting illness and mortality.

• Applications of AI in personalized anti-aging programs

Everyone's physical condition and aging rate are unique. AI can develop personalized anti-aging programs based on an individual's genetic information, lifestyle data, etc. By analyzing an individual's telomere length, gene mutations, and other information, AI can predict the rate of telomere shortening and provide corresponding intervention suggestions. In addition, AI can also provide personalized dietary, exercise, and mental health recommendations based on an individual's health status and needs, thereby achieving comprehensive anti-aging management.

In summary, the application of AI in the field of anti-aging is constantly deepening and developing. With the continuous advancement of technology and the continuous expansion of applications, we have reason to believe that a healthy lifespan of 100 years or even longer will no longer be an unattainable dream. For those pursuing a high-quality life, AI anti-aging is undoubtedly a new field worthy of attention and exploration.

References:

[1] Lyu Y, Fu Q, Wilczok D, Ying K, King A, Antebi A, Vojta A, Stolzing A, Moskalev A, Georgievskaya A, Maier AB, Olsen AGroth Aet al, . Longevity biotechnology: bridging AI, biomarkers, geroscience and clinical Applications for healthy longevity. Aging (Albany NY). 2024 Oct 16; 16:12955-12976 . DOI: 10.18632/aging.206135.

[2] Bennett DF, Goyala A, Statzer C, Beckett CW, Tyshkovskiy A, Gladyshev VN, Ewald CY, de Magalhães JP. Rilmenidine extends lifespan and healthspan in Caenorhabditis elegans via a nischarin I1-imidazoline receptor. Aging Cell. 2023 Feb;22(2):e13774. DOI: 10.1111/acel.13774.

[3] Heckenbach I , Mkrtchyan G , Ezra M B ,et al.Nuclear morphology is a deep learning biomarker of cellular senescence[J].Nature Aging, 2022, 2:742 - 755.DOI:10.1038/s43587-022-00263-3.

[4] Chen, Q., Dwaraka, V. B., Carreras-Gallo, N., Mendez, K., Chen, Y., Begum, S., . . . Lasky-Su, J. A. (2023). OMICmAge: An integrative multi-omics approach to quantify biological age with electronic medical records. bioRxiv, 2023.2010.2016.562114. doi:10.1101/2023.10.16.562114.

[5] Sahoo, D., Swanson, L., Sayed, I. M., Katkar, G. D., Ibeawuchi, S.-R., Mittal, Y., . . . Ghosh, P. (2021). Artificial intelligence guided discovery of a barrier-protective therapy in inflammatory bowel disease. Nature Communications, 12(1), 4246. doi:10.1038/s41467-021-24470-5.