We hypothesize that age‑related changes in the gut microbiome increase circulating secondary bile acids, which activate microglial TLR4‑NFκB signaling and accelerate brain aging. This cascade can be captured by a heterogeneous graph neural network that links microbe nodes, bile‑acid metabolite nodes, and brain gene‑expression nodes, while encoding polypharmacy and diet as edge attributes. If true, then (1) targeted ablation of bile‑acid‑producing bacterial strains in aged mice will reduce hippocampal IL‑1β and Iba1 immunoreactivity by at least 30 % compared with controls, and (2) the gut‑brain GNN will identify known aging interventions (e.g., metformin, rapamycin) with higher precision‑recall scores than brain‑only GNNs trained on sMRI or rs‑fMRI data. To test this, we will construct a multi‑omics cohort from 200 aged mice metagenomics, targeted metabolomics (bile‑acid panel), and single‑cell brain transcriptomics. Microbe‑metabolite edges will be weighted by measured conversion rates; metabolite‑gene edges will reflect bile‑acid‑responsive transcription factor binding sites from public ChIP‑seq datasets. Drug exposure edges will encode prescribed medications and dietary logs. We will train the GNN to predict hippocampal senescence scores and evaluate performance against a brain‑only baseline using five‑fold cross‑validation. Success would demonstrate that incorporating bidirectional gut‑brain messaging improves drug‑target discovery for aging and refutes the notion that the axis is too noisy for mechanistic modeling.