DeepoMe Files IPO Prospectus: Pioneering Saliva-Based DNA Methylation Platform for Multidimensional Aging Quantification and Personalized Longevity Solutions

From Peng Zu, who is said in legend to have lived for over 800 years, to Qin Shi Huang, who sought elixirs of immortality abroad, Chinese mythology and historical records are replete with tales of eternal life.

Behind this lies people’s longing for longevity, a desire that has persisted to the present day. However, alongside market demand have emerged widespread accusations of “snake oil” products and scams. Xiong Jianghui, founder of Beijing Deep Methyl Health Technology Co., Ltd. (hereinafter referred to as “Deep Methyl”), frankly stated, “Indeed, the entire big health industry (including the longevity sector) is currently facing a crisis of trust.”

The Trust Chain Crisis in the Longevity Industry Is Not Unrelated to the Inherent Characteristics of Longevity (Anti-Aging)

The essence of longevity lies in delaying aging, but what is the essence of aging? This question remains unresolved in academia.

Some perspectives define aging as the progressive decline of physiological functions over time; others view it as the accumulation of damage; still others characterize it as a multifactorial, time-dependent process. Regardless of the definition employed, there is a consensus within the field that aging is both systemic and complex.

The systemic and complex nature of aging increases the difficulty of aging detection and intervention.

Biological organisms are highly complex, hierarchically structured, and modular systems, with a degree of complexity that is truly astonishing. From the microscopic molecular level to macroscopic organ function, and from intracellular metabolic regulation to the body’s systemic immune response, the aging process permeates every level and dimension of the organism. In other words, aging is an all-encompassing, multi-layered complex process.

Moreover, aging exhibits highly individualized characteristics. Different individuals possess diverse genetic backgrounds. These genetic differences serve as distinct “blueprints of life,” laying a unique foundation for the aging process. Meanwhile, environmental conditions are in constant dynamic flux; external factors ranging from climate and pollution to life stress continuously influence the trajectory of aging. Furthermore, lifestyles vary significantly among individuals, with differences in dietary habits, exercise frequency, and sleep-wake patterns contributing to the pronounced personalization of aging.

Therefore, a single, holistic biological age metric is far from sufficient to comprehensively and accurately reflect the full spectrum of individual aging and its underlying complex changes. Moreover, the interplay of personalized factors poses significant challenges to the generalizability of research findings. In practical terms, aging-related conclusions drawn for a specific individual or particular population may not be directly applicable to other individuals or groups. Consequently, aging research must take these personalized factors into more detailed consideration, with the aim of achieving results that are both more targeted and broadly applicable.

Fortunately, significant breakthroughs have been made in the scientific understanding of aging. Most notably, the Hallmarks of Aging theory was updated in 2023 to include twelve key hallmarks: genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, impaired macroautophagy, dysregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, chronic inflammation, and dysbiosis (microbiome imbalance). Spanning from the molecular to the systemic level, these twelve hallmarks elucidate the underlying mechanisms by which biological systems deteriorate with age.

In the realm of anti-aging interventions, scientists have also made significant progress. Unfortunately, apart from approaches such as cell therapy and gene editing, which still require validation of their safety and efficacy, common methods including nutritional supplementation, lifestyle modifications, exercise, and physical therapy face challenges related to uncertain effectiveness in practical applications.

The underlying reasons are primarily threefold: First, the aging process involves changes across multiple physiological systems, making it difficult for single-target approaches to achieve comprehensive effects. Second, the quality of anti-aging products on the market varies significantly, with both raw materials and manufacturing processes influencing final efficacy. Third, individuals differ in their physical conditions and aging characteristics, necessitating more personalized solutions.

Furthermore, the aging industry faces a challenge that cannot be overlooked: the increasingly prominent phenomenon of multimorbidity in an aging society. Xiong Jianghui believes that in today’s world, where multimorbidity has become the norm, the R&D approach of “one disease, one model” is no longer applicable. If we merely continue to follow the traditional thinking and technical pathways of medicine and the pharmaceutical industry, the curse of “$1 billion investment, ten-year development cycle, and less than 10% success rate” in new drug development will significantly reduce both the efficiency and confidence in advancing the anti-aging field. Therefore, longevity science urgently needs a new paradigm for research and industrialization.

Meanwhile, Xiong Jianghui also stated that the various challenges currently facing longevity technology are, in essence, rooted in the complexity of life and aging. The original intention behind founding Deep Methyl was precisely to confront this complexity.

DeepMethyl is a longevity technology company focused on the deep integration of epigenetics and artificial intelligence, dedicated to deciphering aging mechanisms through DNA methylation technology and developing multi-dimensional aging detection and intervention solutions.

Xiong Jianghui provided VCBeat with an in-depth analysis of the core philosophy behind “Deep Methyl.” Life systems are akin to a sophisticated “black box,” and understanding aging must not be limited to superficial observations of phenotypic symptoms or biological age.

In the longitudinal dimension, “depth” signifies a layered penetration from macroscopic signs to microscopic essence. It requires an in-depth investigation of subtle aging-related changes across various scales, ranging from disruptions in cellular metabolism and organelle dysfunction to abnormalities in protein homeostasis.

In the horizontal dimension, “Depth” draws on the powerful feature abstraction capabilities of deep learning. Just as AlphaGo automatically extracts the essence of Go through multi-layer neural networks, and DeepFold leverages AI to decipher the rules of protein folding, “Deep Methyl” trains deep models on massive methylation data, enabling algorithms to autonomously uncover patterns of aging characteristics that are difficult for humans to detect.

Methylation acts as a “switch” for gene regulation, serving as a key carrier of information that records the human life cycle, environmental exposures, and the aging process. It can be regarded as the operating system language of the complex machine that is the human body. The goal of Deep Methylation is to precisely articulate the complexity of aging and the mechanisms of efficacy of various anti-aging interventions using the language of methylation. DeepoMe, short for “Deep Representation of Me,” is on a mission to build a robust data foundation for the health and wellness industry using the language of methylation.

From Xiong Jianghui’s presentation, we can derive at least two key insights: first, DNA methylation is the primary “biomarker” employed by Deep Methyl; second, leveraging DNA methylation, Deep Methyl aims to achieve two objectives: aging assessment and data support for aging interventions.

Based on this, DeepMethyl has developed a multi-dimensional aging detection solution. According to Xiong Jianghui, by applying high-throughput sequencing of DNA methylation in saliva samples and signaling pathway-based aging clocks, the multi-dimensional aging detection solution can quantify the twelve major hallmarks of aging that have reached consensus within the academic community.

Building on this foundation, Deep Methylation integrates 18 organ aging indicators (including those for the liver, cardiovascular system, and brain), 15 immune aging indicators (such as T-cell function and cytokines), and metabolic aging indicators (including glucose and lipid metabolism, alcohol metabolism, and free radical scavenging capacity). This forms four major modules: Twelve Major Aging Biomarkers, Organ Aging, Immune Aging, and Metabolic Aging. Consequently, it establishes an aging assessment system that covers multi-level aging phenotypes across organelles, signaling pathways, and organs, achieving full-dimensional coverage from molecules to organs.

Furthermore, the adoption of saliva samples in Deep Methyl’s multi-dimensional aging detection solution represents another innovation. Compared with traditional blood tests, saliva collection is non-invasive and convenient, requiring no professional operation in a medical setting; users can easily complete the process in various scenarios, such as during daily life or while traveling. In addition, saliva samples can be stored and transported at room temperature without the need for complex cold-chain equipment, significantly lowering the barrier to application and enabling health management to become truly integrated into everyday life.

In the interview, Xiong Jianghui also addressed concerns regarding the accuracy of saliva-based testing. “Over the past decade, more than 500 research papers have been published on diseases and aging based on salivary DNA methylation. The DNA methylation signals in saliva primarily originate from three cell types: immune cells, shed epithelial cells, and fibroblasts. By analyzing large-scale reference databases of salivary methylation in population cohorts and processing signals using machine learning techniques such as deconvolution algorithms, the precision of saliva-based testing has become comparable to that of blood testing. For instance, in studies of neurodegenerative diseases such as Parkinson’s disease, salivary methylation testing has demonstrated phenotypic correlations equivalent to those observed with blood sample testing.”

Notably, the Deep Methyl multi-dimensional aging detection solution is not only applicable to aging assessment but also to predictive research on conditions such as benign versus malignant differentiation of pulmonary nodules and depression, with significant progress already achieved. For instance, in a scientific collaboration with Peking University Cancer Hospital on determining the benign or malignant nature of sub-centimeter pulmonary nodules, Deep Methyl’s multi-dimensional aging detection solution demonstrated considerable accuracy, providing molecular-level insights for early screening.

Since initiating the commercialization of its data services for institutions in 2023, Deep Methyl’s aging solutions have been successfully applied in thousands of cases. By identifying personalized aging vulnerabilities and constructing digital life profiles, it has become a novel entry point and starting point for aging intervention and health management.

Specifically, leveraging advanced technologies, Deep Methyl can precisely identify individual vulnerabilities in the aging process, such as accelerated aging in specific organs or more pronounced functional decline in certain physiological systems. Meanwhile, it establishes a comprehensive digital life profile for each individual, recording multidimensional information ranging from physiological metrics to lifestyle habits, thereby laying a solid foundation for subsequent health management.

In the future, Deep Methyl will continue to provide robust data-driven empowerment to various institutions focused on aging and health management. It is committed to delivering scientific and reliable data support for demonstrating the efficacy and characterizing the mechanisms of action of aging intervention strategies. Through these efforts, Deep Methyl aims to help more people better manage the aging process, thereby enhancing the precision and effectiveness of health management.

When discussing the development goals of Deep Methyl, Xiong Jianghui stated that under the impact of artificial intelligence technologies such as DeepSeek, we have come to deeply realize that knowledge expressed in human language will ultimately be mastered by machines. Therefore, there is an urgent need to actively explore domains of knowledge that remain incomprehensible to artificial intelligence. Among these, the complexity of aging is particularly prominent—its foundation consists of intricate networks of interactions, and the effects and mechanisms of drugs and anti-aging technologies likewise form a complex interactive network. Regrettably, most current scientific papers capture only simple nodal patterns within this complex network, such as “Gene A affects Phenotype P via Signaling Pathway B,” and artificial intelligence can currently read only such localized knowledge.

Deep Methyl is dedicated to leveraging the language of methylation to decipher the complexities of aging and to characterize the mechanisms of action underlying various anti-aging interventions. The “language of methylation” refers to the methylation status of gene regulatory regions. Each gene’s regulatory region may exhibit either high or low levels of methylation; typically, the higher the degree of methylation, the more likely the gene is to be silenced. This mechanism can be analogized to a simple switch system: akin to the “0” and “1” in binary code, a gene is either in an “on” state (1) or an “off” state (0).

DeepMethyl’s Salivary Methylation Technology Platform leverages the “language of methylation” to assess, across thousands of dimensions (such as “changes in mitochondrial function” and “changes in chronic inflammation”), the effects of various anti-aging interventions and health management measures on the human body in real-world settings. Historically, such data were accessible only to scientists in laboratory environments, often requiring complex methodologies including cytological experiments, animal studies, and clinical trials. Today, DeepMethyl enables convenient acquisition of these data from saliva samples in diverse scenarios, including at home and while traveling.

When high-dimensional health information can be accessed conveniently and at low cost anytime and anywhere, it will help realize truly personalized treatment plans—tailored prescriptions for each individual. Meanwhile, this will also contribute to resolving the trust crisis facing the broader healthcare sector.

In Xiong Jianghui’s view, a paradox exists within this trillion-yuan industry: despite the rapid growth in demand for health management, market trust is declining. As artificial intelligence gradually assumes more responsibilities in health consultation and recommendations, the lack of standardized data interfaces has become a bottleneck to development. Future marketing will not only target humans but also extend to machines and AI agents, a shift that will depend on the high-content, standardized data carried by methylation language. Looking ahead, Xiong Jianghui firmly believes that methylation language will become a standard component in the broader health and anti-aging sectors, fostering a diverse array of application scenarios.