Hypothesis\nWe propose that the glymphatic system does not merely clear waste; it performs a sleep‑dependent triage that selectively preserves or degrades protein complexes based on their functional integrity, thereby modulating the druggability of aging‑related signaling networks. In other words, the nightly CSF‑ISF exchange acts as a molecular editor that decides which nodes in the aging interactome survive to the next day, and disruption of this editor shifts the network toward pathogenic hub persistence.\n\n## Mechanistic Basis\nDuring slow‑wave sleep, AQP4‑mediated CSF influx increases 90 % and drives convective exchange that lifts soluble proteins, aggregates, and metabolites from the interstitial space 1. Concurrently, norepinephrine levels drop, removing tonic suppression of glymphatic flow 2. We argue that this convective flux is not random; it is biased by the affinity of cargo for perivascular astrocytic end‑feet and by the local concentration of chaperones and ubiquitin ligases that tag misfolded complexes for removal. Consequently, protein assemblies that have sustained damage during wakefulness acquire a higher “degradation score” and are preferentially cleared, whereas intact complexes are spared. This creates a sleep‑gated filter on the aging interactome: nodes such as p53, MAPK/ERK, and PI3K/AKT—identified as age‑specific hubs 3,4—will show fluctuating effective connectivity depending on prior sleep quality.\n\n## Testable Predictions\n1. In aged mice, chronic sleep fragmentation will increase the half‑life of phosphorylated tau and amyloid‑β oligomers in the interstitial fluid, measurable by microdialysis, despite unchanged total protein expression.\n2. Pharmacological enhancement of glymphatic flux with low‑dose carvedilol will reduce the apparent centrality of MAPK/ERK in a temporal protein‑protein interaction network constructed from synaptosome isolates taken after sleep versus wake 5.\n3. Combining an AQP4 agonist (e.g., TGN‑020) with a PI3K/AKT inhibitor will produce synergistic reduction of neurodegenerative markers only when administered during the sleep phase, not during wake, reflecting the conditional druggability highlighted by multilayer network frameworks 6.\n4. Human polysomnography data will show that individuals with reduced slow‑wave sleep exhibit higher edge‑weight stability in aging‑related co‑expression networks derived from blood transcriptomes, indicating less nightly network editing.\n\n## Experimental Design\n- Animal model: 18‑month‑old C57BL/6 mice subjected to 4 weeks of sleep fragmentation (gentle cage tapping every 2 min during dark phase) vs. undisturbed controls.\n- Interventions: (a) vehicle, (b) carvedilol (1 mg/kg i.p.) administered 30 min before light onset, (c) TGN‑020 (AQP4 agonist) 0.5 mg/kg i.p., (d) PI3K/AKT inhibitor (LY294002) 5 mg/kg i.p., and combinatorial groups.\n- Readouts: (i) In vivo two‑photon glymphatic tracer influx (k‑value) to quantify flux; (ii) ISF microdialysis for p‑tau and Aβ42 oligomer concentration over 24 h; (iii) Affinity‑purification mass spectrometry of synaptosomal lysates collected at ZT2 (early sleep) and ZT14 (wake) to build directed, weighted PPI networks; (iv) Network analysis to compute node betweenness and edge‑weight variance across sleep/wake states.\n- Human arm: Correlate polysomnography slow‑wave percentage with longitudinal changes in plasma neurofilament light and with dynamic Bayesian network inference of aging‑related gene co‑expression from monthly blood draws.\n\n## Potential Confounds and Controls\n- Ensure that carvedilol’s adrenergic blockade does not independently alter neuronal activity; include a propranolol control to isolate glymphatic effects.\n- Verify that TGN‑020 does not affect AQP4 expression levels via western blot to distinguish acute gating from chronic compensation.\n- Account for circadian hormone fluctuations by sampling at matched zeitgeber times and measuring corticosterone levels.\n- Use littermate controls and blind outcome assessment to limit bias.\n\nIf the hypothesis holds, sleep‑dependent glymphatic triage will emerge as a conditional layer of network regulation that must be incorporated into temporal GNN models of aging; ignoring it will overestimate the static importance of hubs and misprioritize therapeutic targets.