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Heat stress (HS) causes adverse impacts on pig production and health. A potential biomarker of HS is required to predict its occurrence and thereby better manage pigs under HS. Information about the saliva metabolome in heat-stressed pigs is limited. Therefore, this study was aimed to investigate the effects of acute HS on the saliva metabolome and identify metabolites that could be used as potential biomarkers. Growing pigs (n = 6, 3 boars, and 3 gilts) were raised in a thermal neutral (TN; 25°C) environment for a 5-d adaptation period (CON). After adaptation, the pigs were first exposed to HS (30°C; HS30) and then exposed to higher HS (33°C; HS33) for 24 h. Saliva was collected after adaptation, first HS, and second HS, respectively, for metabolomic analysis using 1 H-nuclear magnetic resonance spectroscopy. Four metabolites had significantly variable importance in the projection (VIP &gt; 1; p &lt; 0.05) different levels in TN compared to HS groups from all genders (boars and gilts). However, sex-specific characteristics affected metabolites (glutamate and leucine) by showing the opposite results, indicating that HS was less severe in females than in males. A decrease in creatine levels in males and an increase in creatine phosphate levels in females would have contributed to a protective effect from protein degradation by muscle damage. The results showed that HS led to an alteration in metabolites related to energy and protein. Protection from muscle damage may be attributed to the alteration in protein-related metabolites. However, energy-related metabolites showed opposing results according to sex-specific characteristics, such as sex hormone levels and subcutaneous fat layer. This study had shown that saliva samples could be used as a noninvasive method to evaluate heat-stressed pigs. And the results in this study could be contributed to the development of a diagnostic tool as a noninvasive biomarker for managing heat-stressed pigs.

Effects of acute heat stress on salivary metabolites in growing pigs: an analysis using nuclear magnetic resonance-based metabolomics profiling