Serum metabolomic biomarkers of response to exercise-heat acclimation

• Published in: Physiology (Bethesda, Md.) Vol. 40; no. S1
• Main Authors: Thornton, Staci, Szymanksi, Michael, Struder, Jeb F., Casa, Douglas J., Lee, Elaine C.
• Format: Journal Article
• Language: English
• Published: 01.05.2025
• ISSN: 1548-9213; 1548-9221
• DOI: 10.1152/physiol.2025.40.S1.1265

Abstract only Objective: Exercise-heat tolerance testing is used to assess return to action, or as part of acclimation strategies to reduce risk of heat illness. Current recommendations are based primarily on research conducted on male subjects with 5% of studies with only females and 25% among the research published on thermoregulation and thermal tolerance in the 10-year period from 2010-2019. This limited inclusivity reduces impact of heat tolerance testing, and key thresholds may be mis-assigned and mis-classified without multi-omic biomarkers. Metabolomics offers a unique entry point to assess heat tolerance with high resolution, as the metabolome provides a sensitive snapshot of physiological and biological status. We hypothesize serum metabolomic shifts will predict responder status following a heat acclimation protocol combined with moderate-intensity exercise. Methods: Participants (n=19) completed testing in naive state that includes heat tolerance testing (HTT) and 5 days of heat acclimation (HA) in 40°C, 40% relative humidity. Metabolomes were analyzed from serum collected pre and post exercise, pre and post heat tolerance day 2 (HTT2, 65% vVO2max at 2% incline). Subjects were classified into responder status based on ability to pass the HTT2 (reach core temperature of 39.5°C). Fail-Fail group (FF) (n=6), Fail-Pass (PF) (n=6), Pass-Pass (PP) (n=7). Extracted metabolomes normalized to 10mg/mL (1:1, 100% methanol:water) were run in positive and negative mode Waters SynaptG2-Si (mass range 80-2000, resolution mode), peaks aligned to Metlin database using ProgenesisIQ software. Results: Between groups, differences in BMI (kg-(·m2)-1) were significant between the FF and FP groups while VO2-max (mL·kg - 1 ·min - 1 ) was significantly different between the FP and PP groups. One differentially abundant metabolite is significant (p<0.05) considering all subjects post-exercise preHA vs postHA; lysophosphatidylcholine (LysoPC, (m/z 468.31)), is a class of metabolite with protective or anti-inflammatory effect. pre vs post-exercise preHA significantly altered abundance of 1 metabolite (glycerophospholipid) and 2 pre vs post-exercise postHA (cholesterol metabolism). Metabolome shifts are evident when stratifying by responder; change in abundance (ratio post-exercise postHA/preHA) of Acetylcarnitine (FF vs PP), Octadecadiynoic Acid (FP vs PP), Lys Ile Thr (FP vs PP), m-Coumaric acid (FF vs FP), Linoleic Acid (FF vs FP vs PP), and 9-Thiasteric acid (FP vs PP) (p<0.05). Leucine enkephalin and γ -aminobutyric acid concentrations significantly (p<0.05) change in a responder-specific manner across HA. Conclusion: Serum metabolome profile shifts are predominant when metabolites are stratified by response to HA. Opioid and inhibitory neurotransmission may play a role in ability to respond to exercise-HA. DoD BA200299, COR2E Core Incentive Plan. DoD BA200299, COR 2 E Core Incentive Plan. This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.