AI and epigenetics: new approaches to skin aging understanding and treatment

Artificial intelligence (AI) and epigenetics are transforming our comprehension of skin aging. Epigenetic modifications, such as DNA methylation, histone modification, and non-coding RNA regulation, play key roles in natural and pathological aging by reducing tissue regenerative capacity and promoting the accumulation of senescent cells.1, 2 These complicated processes have historically been challenging to explain. Still, AI is proving to be an important tool in showing the epigenetic mechanisms underlying skin aging, thereby offering the promising potential for targeted therapies (Figure 1). One notable area of interest is telomere shortening, an external factor influencing skin aging. Telomeres, which are protective caps located at the ends of chromosomes, shorten with each cell division. Factors such as UV exposure and oxidative stress accelerate this shortening, triggering epigenetic changes that block stem cell function in keratinocytes and dermal fibroblasts. This contributes to reduced skin renewal as a person ages. For instance, oxidative stress exacerbates telomere erosion by increasing the rate of DNA damage, leading to accelerated aging. AI can help clear the complex interactions between these factors, identifying the most impactful ones and offering insights into how interventions might slow or reverse telomere shortening.3 AI's potential goes beyond identifying epigenetic alterations; it also holds promise for developing personalized anti-aging therapies. In this context, anti-aging therapies include molecular-level interventions, containing those targeting telomere maintenance or reversing epigenetic modifications. Additionally, exosomes, which are extracellular vesicles containing proteins, lipids, and RNA, have emerged as a promising tool in this area. Exosomes can facilitate cell-to-cell communication, delivering molecules that can influence epigenetic regulation and potentially rejuvenate aging skin. AI can play an important role in identifying which exosomal cargo is most effective in modulating the epigenetic clock, offering new avenues for research and therapy. The epigenetic clock, which estimates biological age based on DNA methylation patterns, presents one of the most promising areas for AI application. AI can refine these clocks by integrating multi-omics data such as transcriptomics and metabolomics, providing a more comprehensive understanding of the molecular interactions driving aging. While current research predominantly focuses on aging, these insights can also be extended to autoimmune and inflammatory skin diseases, where epigenetic dysregulation plays a significant role. For example, AI-guided studies have shown promise in identifying specific epigenetic markers associated with diseases like psoriasis and lupus, expanding the potential impact of AI-powered epigenetic research. A prominent real-world application is the identification of blood methylation markers that reflect facial aging, demonstrating how AI can correlate these markers with visible signs of aging. Furthermore, AI tools, including machine learning, deep learning, and graph neural networks, are instrumental in analyzing large datasets and recognizing epigenetic patterns linked to skin aging and diseases. For instance, AI algorithms have been utilized in HIV research, uncovering how epigenetic alterations contribute to accelerated aging in HIV-positive individuals, which may have dermatological relevance.4 Understanding epigenetic aging involves the “Horvath Clock,” a well-established tool for assessing biological age through DNA methylation patterns. Recent research has revealed different strategies to slow down this clock, such as dietary adjustments, medications, and lifestyle changes. By using AI, we could speed up the identification and development of these interventions, leading to more accurate and personalized treatments based on each person's epigenetic structure.5 In conclusion, AI is well-positioned to extend our understanding of skin aging due to its ability to analyze complex epigenetic data. As research advances, AI-driven insights will lead to personalized, targeted, and effective anti-aging therapies with broader implications for the treatment of other skin diseases. We confirm that the manuscript has been read and approved by all the authors, that the requirements for authorship as stated earlier in this document have been met, and that each author believes that the manuscript represents honest work.