RootMap: A Longevity Medicine Framework for Mapping Conditional Dependencies Between Aging Hallmarks and Organ-Aging Patterns

Longevity medicine needs ways to identify a small number of candidate intervention targets that might affect several functions at once. Protein–protein interaction (PPI) networks help map molecular connections and rank candidate drugs, but they do not show which maintenance states are associated with age-related patterns in particular organs. We introduce RootMap, a blood DNA-methylation framework for mapping these conditional dependencies, and apply it to 656 discovery and 1,394 replication participants. Each of 332 gene modules is scored as a module-level intrinsic capability (mIC). We define a conditional dependency as a difference in an organ module’s association with age between participants with higher versus lower age-adjusted mIC for a hallmark module. Twenty-two hallmark–organ pairs passed four artifact checks; 17 of 20 pairs on the replication list kept their direction in the second cohort, including all 10 testable organ-line pairs. The pattern was asymmetric: hallmark modules more often marked differences in organ–age associations than organs marked differences in hallmark–age associations. Stem-cell maintenance was the broadest marker in the discovery cohort, relating to 12 organ modules; senescence regulation was broadest in the replication cohort. The meaning of dependency is illustrated by bone marrow: among people with higher age-adjusted stem-cell-maintenance mIC, bone-marrow mIC was negatively associated with age (r = −0.241), while among those with lower mIC the association was close to zero (r = +0.005). The map also distinguishes organs by their patterns of dependencies: for example, the liver had several hallmark associations, including associations in opposite directions. These are patterns in blood-based scores, not proof of biological control. The map may help drug screening by adding information about biological layer and dependency to molecular proximity. In a benchmark, filtering a protein-interaction (PPI)-ranked anti-aging candidate list by functional-layer labels made the list 2.8 times more compact and increased hit enrichment from 3.19-fold to 8.87-fold. This gain was not distinguishable from draws matched for the number of drug targets (p = 0.18), and the filter used layer labels rather than learned dependency edges. Thus, the result supports testing layer information alongside PPI—not claiming that dependency edges have already improved drug discovery. Separately, discovery dependency effect sizes were associated with replication dependencies (ρ = +0.153, p = 0.005), whereas the tested PPI-proximity measures were not. Functional modules also showed intervention-associated changes more often than hallmark modules (25.4% versus 14.4% of tests); this measures response in the studied intervention series, not clinical benefit. Traditional Chinese medicine (TCM) concepts were analyzed separately within the same framework. Essence was the largest hub, contributing 5 of 10 significant fundamental-substance pairs. The TCM concept “liver” corresponded more strongly to lymph and immune modules than to the anatomical liver (r = −0.15), illustrating that a traditional concept need not map onto the organ with the same name. RootMap offers longevity medicine a framework for connecting foundational maintenance states with organ-aging patterns. By distinguishing capabilities associated with broad reach from functions that showed more frequent score changes in the intervention series, it gives researchers a practical basis for deciding what to measure, which candidate targets to investigate, and which functions to follow in longitudinal and N-of-1 studies. The dependency measures carried modest but reproducible information across cohorts beyond the PPI-proximity measures tested here, while layer-label filtering compacted a PPI-ranked candidate list in the screening benchmark. Together, these findings position RootMap as an evidence-graded map for prioritizing hypotheses in longevity research and as a foundation for future individual-level biomedical world models. Learned dependency edges and absolute mIC values remain priorities for further validation and calibration.