# Metabolic Overdrive in Elite Sport: A Systems Model of AMPK–mTOR Oscillation, NAD+ Economy, and Epigenetic Drift

## TL;DR

This paper introduces a new model explaining how intense exercise affects metabolism and gene regulation through oscillations between energy-saving and growth states.

## Contribution

The Metabolic Overdrive Model reframes exercise adaptation as a loss of oscillation between AMPK and mTOR signaling.

## Key findings

- Prolonged anabolic signaling after exercise may lead to suppressed AMPK and altered NAD+ economy.
- The model links metabolic stress to epigenetic changes like histone acetylation and DNA methylation.
- It provides testable predictions for molecular signatures of sustained metabolic stress.

## Abstract

Exercise adaptation depends on a dynamic alternation between catabolic and anabolic states coordinated primarily by AMP-activated protein kinase (AMPK) and mechanistic target of rapamycin (mTOR). While transient activation of these pathways underpins beneficial molecular remodeling, the system-level consequences of sustained anabolic drive remain insufficiently conceptualized in exercise biology. This article presents a conceptual mechanistic narrative review integrating evidence from molecular nutrition, exercise physiology, redox biology, and epigenetic regulation to define limits of adaptive signaling. We propose the Metabolic Overdrive Model, a systems-level framework describing the transition from adaptive AMPK–mTOR oscillation to a high-anabolic lock-in state characterized by persistent mTORC1 activation, suppressed AMPK signaling, altered NAD+ economy (SIRT1–PARP imbalance), redox dysregulation, and progressive epigenetic drift. Using exercise and training as models of sustained metabolic stress, we synthesize mechanistic parallels across energy sensing, oxidative signaling, and chromatin regulation without implying pathological causality. The framework generates testable predictions linking prolonged post-exercise anabolic signaling (>24 h) to specific molecular signatures, including AMPK phosphorylation status, NAD+ availability, PARylation, histone acetylation, and DNA methylation dynamics. By reframing exercise adaptation as a loss-of-oscillation phenomenon rather than a linear continuum, this model provides a mechanistic language for hypothesis generation, biomarker-guided periodization, and future experimental validation.