Factors Influencing AMPK Activation During Cycling Exercise: A Pooled Analysis and Meta-Regression

Abstract

Background: The 5' adenosine monophosphate (AMP)-activated protein kinase (AMPK) is a cellular energy sensor that is activated by increases in the cellular AMP/adenosine diphosphate:adenosine triphosphate (ADP:ATP) ratios and plays a key role in metabolic adaptations to endurance training. The degree of AMPK activation during exercise can be influenced by many factors that impact on cellular energetics, including exercise intensity, exercise duration, muscle glycogen, fitness level, and nutrient availability. However, the relative importance of these factors for inducing AMPK activation remains unclear, and robust relationships between exercise-related variables and indices of AMPK activation have not been established.

Objectives: The purpose of this analysis was to (1) investigate correlations between factors influencing AMPK activation and the magnitude of change in AMPK activity during cycling exercise, (2) investigate correlations between commonly reported measures of AMPK activation (AMPK-α2 activity, phosphorylated (p)-AMPK, and p-acetyl coenzyme A carboxylase (p-ACC), and (3) formulate linear regression models to determine the most important factors for AMPK activation during exercise.

Methods: Data were pooled from 89 studies, including 982 participants (93.8% male, maximal oxygen consumption [[Formula: see text]] 51.9 ± 7.8 mL kg^-1 min^-1). Pearson's correlation analysis was performed to determine relationships between effect sizes for each of the primary outcome markers (AMPK-α2 activity, p-AMPK, p-ACC) and factors purported to influence AMPK signaling (muscle glycogen, carbohydrate ingestion, exercise duration and intensity, fitness level, and muscle metabolites). General linear mixed-effect models were used to examine which factors influenced AMPK activation.

Results: Significant correlations (r = 0.19-0.55, p < .05) with AMPK activity were found between end-exercise muscle glycogen, exercise intensity, and muscle metabolites phosphocreatine, creatine, and free ADP. All markers of AMPK activation were significantly correlated, with the strongest relationship between AMPK-α2 activity and p-AMPK (r = 0.56, p < 0.001). The most important predictors of AMPK activation were the muscle metabolites and exercise intensity.

Conclusion: Muscle glycogen, fitness level, exercise intensity, and exercise duration each influence AMPK activity during exercise when all other factors are held constant. However, disrupting cellular energy charge is the most influential factor for AMPK activation during endurance exercise.