Department of Biomedical Engineering, Binghamton University-State University of New York, Binghamton, NY, 13902, USA.
Health and Wellness Studies Department, Binghamton University-State University of New York, Binghamton, NY, 13902, USA.
Sci Rep. 2020 Nov 4;10(1):19050. doi: 10.1038/s41598-020-75871-3.
Cortisol is a biomarker for stress monitoring; however, the biomedical and clinical relevance is still controversial due to the complexity of cortisol secretion mechanisms and their circadian cycles as well as environmental factors that affect physiological cortisol level, which include individual mood and dietary intake. To further investigate this multifaceted relationship, a human pilot study examined cortisol concentration in sweat and saliva samples collected from 48 college-aged participants during aerobic exercise sessions along with mental distress and nutrition surveys. Enzyme-linked immunosorbent assays determined highly significant differences between apocrine-dominant sweat (AP), saliva before exercise (SBE), and saliva after exercise (SAE) cortisol concentration (AP-SBE: p = 0.0017, AP-SAE: p = 0.0102). A significantly greater AP cortisol concentration was detected in males compared to females (p = 0.0559), and significant SAE cortisol concentration differences were also recorded between recreational athletes and non-athletes (p = 0.044). However, Kessler 10 Psychological Distress Scale (K10) scores, an examination administered to deduce overall wellness, provided no significant differences between males and females or athletes and non-athletes in distress levels, which statistically signifies a direct relationship to cortisol was not present. For further analysis, dietary intake from all participants was considered to investigate whether a multiplexed association was prevalent between nutrition, mood, and cortisol release. Significant positive correlations between AP cortisol, SAE cortisol, K10 scores, and fat intake among female participants and athletes were discovered. The various machine learning algorithms utilized the extensive connections between dietary intake, overall well-being, sex factors, athletic activity, and cortisol concentrations in various biofluids to predict K10 scores. Indeed, the understanding of physiochemical stress response and the associations between studied factors can advance algorithm developments for cortisol biosensing systems to mitigate stress-based illnesses and improve an individual's quality of life.
皮质醇是压力监测的生物标志物；然而，由于皮质醇分泌机制及其昼夜节律的复杂性以及影响生理皮质醇水平的环境因素，包括个体情绪和饮食摄入，其生物医学和临床相关性仍存在争议。为了进一步研究这种多方面的关系，一项针对大学生的人体初步研究调查了 48 名大学生在有氧运动过程中从顶泌汗腺汗液和唾液样本中收集的皮质醇浓度，以及心理困扰和营养调查。酶联免疫吸附测定法确定了顶泌汗腺（AP）为主的汗液（AP）、运动前唾液（SBE）和运动后唾液（SAE）皮质醇浓度之间存在显著差异（AP-SBE：p=0.0017，AP-SAE：p=0.0102）。与女性相比，男性的 AP 皮质醇浓度明显更高（p=0.0559），且在休闲运动员和非运动员之间也记录到了 SAE 皮质醇浓度的差异（p=0.044）。然而，凯斯勒 10 项心理困扰量表（K10）评分，一项用于推断整体健康状况的检查，在困扰水平上未显示男性和女性或运动员和非运动员之间的显著差异，这从统计学上表明皮质醇之间不存在直接关系。为了进一步分析，考虑了所有参与者的饮食摄入，以调查营养、情绪和皮质醇释放之间是否存在多关联。在女性参与者和运动员中，AP 皮质醇、SAE 皮质醇、K10 评分和脂肪摄入量之间发现了显著的正相关。各种机器学习算法利用了饮食摄入、整体健康状况、性别因素、体育活动以及各种生物流体中皮质醇浓度之间的广泛联系，来预测 K10 评分。事实上，对生理化学应激反应的理解以及研究因素之间的关联可以促进皮质醇生物传感系统的算法发展，以减轻应激相关疾病并提高个人的生活质量。
