000165722 001__ 165722
000165722 005__ 20260113234335.0
000165722 0247_ $$2doi$$a10.1038/s41598-025-29572-4
000165722 0248_ $$2sideral$$a147306
000165722 037__ $$aART-2026-147306
000165722 041__ $$aeng
000165722 100__ $$aFrempong, Gifty Animwaa
000165722 245__ $$aUncovering the metabolic impact of acute psychological stress in young adults
000165722 260__ $$c2026
000165722 5060_ $$aAccess copy available to the general public$$fUnrestricted
000165722 5203_ $$aStress is associated with the onset of various neurological disorders, such as depression, post-traumatic stress disorder, and anxiety. Although extensively studied, the metabolic changes triggered in response to stress remain unclear. We conducted a descriptive observational study on acute stress responses in university students, combining psychometric, biochemical, and untargeted metabolomic analyses, along with machine learning predictions. In this study, forty participants underwent both relaxation and stress induction through a modified Trier Social Stress Test. Validated psychometric tests confirmed proper induction of both states. Although most biomarkers show significant changes under acute stress state, the machine learning predictive model identified salivary α-amylase and the State-Trait Anxiety Inventory-state (STAI-s) as potential stress markers. Additionally, several metabolic pathways, including steroid hormone biosynthesis, glycerophospholipid metabolism, linoleic acid metabolism, tyrosine metabolism, and aminoacyl-tRNA biosynthesis, presented alterations under acute mental stress. Our findings highlight the impact of acute mental stress on multiple metabolic pathways directly implicated in stress-related disorders. These findings advance the understanding of the adverse effects systematically associated with stress and provide evidence supporting the potential role of salivary α-amylase and STAI-s as stress markers. Yet, they should be regarded as important hypothesis generators. However, further studies are needed for final validation.
000165722 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T39-23R$$9info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131106B-I00
000165722 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
000165722 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000165722 700__ $$0(orcid)0000-0003-4010-849X$$aGoñi, Guillermina$$uUniversidad de Zaragoza
000165722 700__ $$aLorenzo-Tejedor, Mónica
000165722 700__ $$0(orcid)0000-0003-2284-7862$$aDe la Cámara, Concepción$$uUniversidad de Zaragoza
000165722 700__ $$0(orcid)0000-0001-8742-0072$$aLázaro, Jesús$$uUniversidad de Zaragoza
000165722 700__ $$aMangialavori Rasia, Eugenia
000165722 700__ $$aAguiló, Jordi
000165722 700__ $$0(orcid)0000-0003-1272-0550$$aBailon, Raquel$$uUniversidad de Zaragoza
000165722 700__ $$0(orcid)0000-0002-8222-1418$$aBernal, María Luisa$$uUniversidad de Zaragoza
000165722 7102_ $$11012$$2410$$aUniversidad de Zaragoza$$bDpto. Farmac.Fisiol.y Med.L.F.$$cÁrea Fisiología
000165722 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000165722 7102_ $$11012$$2315$$aUniversidad de Zaragoza$$bDpto. Farmac.Fisiol.y Med.L.F.$$cÁrea Farmacología
000165722 7102_ $$11007$$2745$$aUniversidad de Zaragoza$$bDpto. Medicina, Psiqu. y Derm.$$cArea Psiquiatría
000165722 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000165722 773__ $$g(2026), [44 pp.]$$pSci. rep. (Nat. Publ. Group)$$tScientific reports (Nature Publishing Group)$$x2045-2322
000165722 8564_ $$s1235417$$uhttps://zaguan.unizar.es/record/165722/files/texto_completo.pdf$$yVersión publicada
000165722 8564_ $$s1348795$$uhttps://zaguan.unizar.es/record/165722/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000165722 909CO $$ooai:zaguan.unizar.es:165722$$particulos$$pdriver
000165722 951__ $$a2026-01-13-22:06:54
000165722 980__ $$aARTICLE