New preprint connects foundational biological capabilities with organ-aging patterns and places aging hallmarks and Traditional Chinese Medicine concepts in a shared analytical space
BEIJING, China — September 28, 2026 — Physicians practicing longevity and functional medicine often work backward from a patient’s pattern of symptoms and test results to ask what underlying dysfunction might connect them. Root-cause thinking is also present, in a different conceptual language, in Traditional Chinese Medicine (TCM). Yet clinical experience alone does not provide a shared, testable map of how foundational biological capabilities relate to organ-level patterns.
Aging hallmarks offer a useful vocabulary for organizing age-related biology. But naming a hallmark as a possible explanation for a phenotype does not, by itself, describe how that process relates to a particular organ pattern—or show whether the proposed relationship holds across data.
DeepoMe today announced a preprint introducing RootMap, a framework for mapping and testing these relationships. The paper presents RootMap as a foundational research framework for making root-cause hypotheses more explicit: defining the biological modules involved, measuring them, and testing how their associations vary across people.
RootMap is one module of SteeraMed, DeepoMe’s steerable biomedical world model. The SteeraMed research program includes a steerability framework, N-of-1 intervention reasoning, SteeraMed Bench for evaluating human-scale biological representations, and RootMap’s map of conditional relationships between foundational capabilities and organ-aging patterns. These are connected research components, not a claim that one clinically validated system is complete. Learn more at SteeraMed.com.
The study uses blood DNA-methylation data to score 332 gene modules as module-level intrinsic capabilities (mICs). These include modules derived from aging hallmarks, organ and other functional domains, and TCM concepts. Scoring them within one analytical framework places the concepts in a shared feature space for comparison. It does not make the traditions’ theories equivalent, or establish that one validates the other.
A measurable map of conditional relationships
The analyses used a discovery cohort of 656 participants and an independent replication cohort of 1,394. In the discovery analysis, 22 aging-hallmark–organ pairs passed four checks intended to assess potential artifacts. Among the 20 pairs on the replication list that could be tested in the second cohort, 17 retained the same direction of association; seven also met the study’s multiple-testing threshold there.
The authors define these relationships as conditional associations: the association between an organ-module score and age differed between groups with higher and lower age-adjusted scores for a hallmark-derived module. The results do not show that changing a hallmark causes an organ function to change.
Comparing concepts without assuming they are the same
The common feature space also lets researchers examine how TCM concepts relate to measured biological modules without assuming that a traditional term maps directly to a modern organ with the same name. For example, after accounting for shared score patterns, the TCM “liver” module was only weakly correlated with the anatomical liver module and showed stronger associations with lymph and immune modules. This is a data-defined pattern for further study, not confirmation of a classical TCM mechanism.
An early research framework, not a clinical rule
The paper also explores whether biological-layer information could complement protein–protein interaction (PPI)-based candidate ranking. In one screening analysis, a filter based on pre-assigned module-layer labels increased enrichment of known aging-related compounds. However, the result was not distinguishable from random lists matched for drug-target count, and the filter did not use RootMap’s learned dependency relationships. The authors describe this result as exploratory, not evidence that RootMap has improved drug discovery.
“Clinicians have long sought the underlying causes that could connect a person’s symptoms and organ-level findings. The challenge is turning that search into a framework that can be measured, compared and tested. RootMap is a step toward that foundation. It does not establish causal pathways, but it gives researchers a way to examine relationships between foundational biological capabilities and organ-aging patterns.” — Dr. Jianghui Xiong, founder of DeepoMe
Research status and limitations
The manuscript is a preprint and has not yet undergone peer review. The analyses use observational data: RootMap’s “dependencies” describe differences in associations, not demonstrated causal pathways. The study does not establish that changing a hallmark changes an organ’s aging pattern, predict an individual’s response to treatment, or demonstrate clinical utility. The authors also report that some findings depend on how gene modules are constructed and that certain relationships were sensitive to the scoring procedure.
Availability
Preprint: “RootMap: A Longevity Medicine Framework for Mapping Conditional Dependencies Between Aging Hallmarks and Organ-Aging Patterns”
Preprints.org — manuscript 202609.2376/v1
About DeepoMe
DeepoMe (Beijing DeepoMe Health Technology Co., Ltd.) is a bio-computing company whose stated vision is to make aging “computable and programmable.” Its broader ambition is to make high-dimensional health information easier to access, so more individualized health management becomes practical. The company describes its model as combining DNA-methylation measurement, longevity-medicine services and data feedback.
Capome® is DeepoMe’s saliva DNA-methylation-based, multidimensional aging assessment. It covers aging hallmarks and organ, immune and metabolic aging dimensions. In the company’s product system, Capome provides a measurement input for follow-up and health-management decisions; it is not a disease diagnosis.
DeepKang® is DeepoMe’s AI-enabled longevity-medicine service platform. It is designed to organize personal state information, root-cause analysis workflows, evidence-linked action options and follow-up for professional review. The system is intended to support—not replace—qualified professionals.
SteeraMed® is DeepoMe’s steerable biomedical world-model framework. Its research program includes the steerability framework, N-of-1 intervention reasoning, SteeraMed Bench for evaluating human-scale biological representations, and RootMap. Together, Capome measures biological state, DeepKang supports service delivery and follow-up, and SteeraMed provides the model framework the company is developing to organize states, interventions, evidence and feedback. The longer-term data-feedback vision remains a development agenda, not an outcome established by the RootMap study.
Company website: DeepoMe.com
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