Hypothesis

We propose that integrating mitochondrial redox-sensitive signaling pathways—specifically the NAD+/NADH ratio and ROS-mediated activation of Nrf2/KEAP1—into aging digital twins creates a closed-loop system where real-time biomarker fluxes directly modulate intervention dosing, transforming static forecasts into adaptive therapeutic agents.

Mechanistic Rationale

Current digital twins rely on intermittent biomarker snapshots (epigenetic clocks, CRP, wearables) to predict trajectories but lack fast‑acting physiological readouts that reflect instantaneous cellular stress. Mitochondrial redox state changes within minutes to hours in response to nutrients, exercise, or drugs, and can be sampled non‑invasively via circulating cell‑free DNA oxidation signatures or wearable‑detectable NADH fluorescence. By feeding these dynamic redox indices into the AI core, the twin can instantly simulate the impact of a candidate intervention on downstream pathways (e.g., mTOR inhibition, senescent cell clearance) and adjust dosing before maladaptive cascades accumulate.

Testable Predictions

1. In a cohort of older adults undergoing rapamycin supplementation, real‑time NAD+/NADH ratios measured via a wearable biosensor will predict short‑term changes in epigenetic age acceleration (ΔDNAmAge) more accurately than baseline CRP or IL‑6 levels.
2. Digital twin simulations that incorporate redox feedback will reduce the variance of predicted frailty index trajectories by at least 30% compared with models using only static multimodal inputs.
3. Closed‑loop adjustment of rapamycin dose guided by redox‑triggered alerts will achieve equivalent or superior improvement in gait speed with lower cumulative drug exposure than fixed‑dose regimens in a randomized crossover trial.

Experimental Design

• Enroll 120 participants aged 70‑85, stratify by sex and baseline frailty.
• Equip each with a multimodal wearable that records heart rate variability, activity, and a prototype NAD+/NADH fluorescence sensor (validated against plasma metabolomics).
• Collect baseline multimodal data (epigenetic clock, proteomics, EHR) to initialize each participant's digital twin per 1 and 2.
• Randomize to either (a) standard care with quarterly clinician‑reviewed twin predictions, or (b) closed‑loop arm where the twin continuously ingests redox data and recommends rapamycin dose adjustments via a clinician-in-the-loop interface.
• Primary outcome: change in frailty index over 6 months; secondary: epigenetic age acceleration, incidence of adverse events, total rapamycin exposure.

Potential Challenges and Mitigations

• Sensor accuracy: address by calibrating wearable redox readouts against monthly blood draws and using error‑correction layers in the AI model 4.
• Algorithmic opacity: employ explainable‑AI modules to surface which redox features drove dose changes, satisfying transparency requirements 5.
• Generalizability: include diverse socioeconomic strata to counter performance biases noted in prior work 5.

If validated, this hypothesis would transform digital twins from prognostic dashboards into physiologically responsive actuators, fulfilling the promise of precision gerontology.