INTRODUCTION: The impact of low energy availability (LEA) on metabolic processes has been widely documented in theliterature, with notable alterations observed in various metabolic, endocrine and physiological pathways, e.g., sex hor-mones as well as indicators of bone and iron metabolism. However, a comprehensive understanding of the metabolicperturbations associated with LEA remains elusive. Metabolomics, capable of analyzing a vast array of metabolites atonce, provides a unique opportunity to uncover the potentially complex metabolic signature of LEA, which holds promisefor improved detection and characterization of LEA status.
METHODS: In this study, we employed nuclear magnetic resonance-based metabolomics to quantify 250 metabolites andmetabolite ratios in post-intervention blood samples obtained following short-term exposure to LEA (15 kcal/kg fat-freemass (FFM)/day) and normal EA as control (CON; 40 kcal/kg FFM/day). Blood samples utilized in our analysis weresourced from two larger crossover design studies (n=13, 85% males, aged 23.2±3.5 years), one of which involved dailyaerobic exercise across both conditions, expending 15 kcal/kg FFM/day. We used generalized estimating equations toevaluate the effects of LEA on metabolite concentrations, while employing multiple logistic regression to predict LEA statusbased on metabolic profiles.
RESULTS: We observed significant condition effects in 120 out of 250 metabolites, independent of exercise. Notably, triglyc-erides (LEA vs. CON: 0.63±0.20 vs. 0.99±0.44 mmol/L, adjusted p<0.05), fatty acids (9.22±1.38 vs. 10.65±2.51 mmol/L,adjusted p<0.05), ketone bodies (0.30±0.25 vs. 0.03±0.02 mmol/L, adjusted p<.001) and very-low density lipoprotein(VLDL) sub-classes (adjusted p<0.05) exhibited significant differences. Furthermore, the stepwise inclusion of these varia-bles into a logistic regression model demonstrated their ability in predicting LEA status (LEA ~ Acetoacetate + Total triglyc-erides + Ratio of saturated fatty acids to total fatty acids, AIC=18.3, p<.001).
CONCLUSION: Our analysis revealed significant group differences across a broad spectrum of metabolites, indicative of atransition towards increased fat utilization, ketosis, VLDL lipolysis and lipid transfer to high-density lipoprotein particles.These findings underscore the potential of metabolomics for identifying the metabolic signature of LEA, which may in turnbe used to identify individuals currently exposed to LEA