Current aging models treat autophagy as a monolithic process. They're wrong. Selective autophagy operates via a precise degradation hierarchy mediated by selective autophagy receptors (SARs) with specific AIM/LIR motifs, prioritizing different cargos under stress [https://pmc.ncbi.nlm.nih.gov/articles/PMC5549613/]. This hierarchy isn't uniform; individual SAR expression profiles create unique "degradation fingerprints."

Hypothesis: An individual's baseline autophagy hierarchy profile, defined by the relative expression and activity of SARs (e.g., OPTN, NDP52, p62 for aggrephagy; BNIP3L, FUNDC1 for mitophagy), determines their specific aging trajectory and response to pro-autophagic interventions. Disrupting this sequence—not just reducing overall flux—is a primary driver of age-related decline.

Mechanistic Rationale:

1. Hierarchy as a Clock: The sequence (e.g., protein aggregates before organelles) may encode an intrinsic cellular survival priority. Age-associated transcriptomic downregulation of specific autophagy pathways [https://doi.org/10.1101/2025.01.14.632938] likely perturbs this sequence, causing accumulation of higher-priority damaged components (e.g., depolarized mitochondria) while lower-priority cargo is still cleared.
2. Biomarker Stratification: Existing plasma miRNA panels and hub genes (TFEB, TOMM20, GABARAPL1) that predict aging/AD with high AUC [https://www.frontiersin.org/journals/molecular-neuroscience/articles/10.3389/fnmol.2025.1588007/full][https://pmc.ncbi.nlm.nih.gov/articles/PMC11354206/] likely reflect disruptions at different nodes of this hierarchy. A high-TFEB/low-TOMM20 profile suggests a mitophagy-specific bottleneck, not a global autophagy deficit.
3. Digital Twin Integration: Current digital twins lack this mechanistic layer [https://pmc.ncbi.nlm.nih.gov/articles/PMC12371080/]. We propose a computational module that simulates an individual's autophagic flux using their unique SAR expression profile. This would predict whether a senolytic or mTOR inhibitor intervention would effectively clear damaged mitochondria or, conversely, cause proteotoxic shock by prioritizing organelle degradation over aggregate clearance.

Testable Predictions & Falsification:

• Prediction 1: In longitudinal cohorts, individuals with a "high mitophagy/low aggrephagy" SAR profile will show earlier onset of mitochondrial dysfunction phenotypes (e.g., metabolic shifts) but later onset of protein aggregation diseases (e.g., amyloidopathies). Falsified if no temporal correlation exists between SAR profile and specific age-related pathologies.
• Prediction 2: Rapamycin treatment will differentially extend healthspan based on hierarchy profile. Those with aggrephagy-prioritized profiles will respond better. Falsified if response correlates only with age or global autophagy markers, not the specific hierarchy.
• Test: A trial where participants are stratified by a composite autophagy hierarchy score (using exosomal miRNA + tissue-specific SAR expression) before receiving low-dose rapamycin. Primary outcome: tissue-specific clearance of damaged organelles/proteins (via novel PET tracers) versus placebo, predicted by their pre-assigned hierarchy profile.

The autophagy hierarchy isn't background noise—it's the operating system for cellular resource allocation during aging. Modeling it is the missing link for personalized geroscience.