Environmental Heat Stress Modulates Systemic Redox Homeostasis: A Longitudinal marker Analysis in Healthy Adults

Environmental Heat Stress Modulates Systemic Redox Homeostasis: A Longitudinal marker Analysis in Healthy Adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Environmental Heat Stress Modulates Systemic Redox Homeostasis: A Longitudinal marker Analysis in Healthy Adults Hong Zhou, Jiachen Dai, Yuemei Zhang, Wenwen Tang, Yingying Ren, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7350440/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Against the backdrop of more frequent extreme heat events driven by climate change and urbanization, clarifying the physiological effects of environmental heat exposure—particularly its impact on redox homeostasis in healthy populations—and identifying biomarkers for early heat-induced stress detection have become urgent public health priorities. This longitudinal study enrolled 330 healthy volunteers from four cities in Hubei Province (China), with fasting serum samples collected in March (mild temperatures) and August (high temperatures) to measure and analyze six redox markers: nitrite (NO₂⁻), nitrate (NO₃⁻), oxidized glutathione (GSSG), malondialdehyde (MDA), and total antioxidant capacity (assessed via FRAP and ABTS assays). High-temperature exposure significantly disrupted redox balance, characterized by decreased nitric oxide bioavailability, elevated lipid peroxidation, and enhanced serum antioxidant activity. Among the measured markers, MDA exhibited the most sensitive and consistent response to heat stress, with little interference from age or sex—supporting its potential as a reliable indicator for evaluating heat-induced oxidative damage. These insights may contribute to reducing health risks linked to extreme heat exposure. heat stress malondialdehyde nitric oxide antioxidant redox marker Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The intensification of the urban heat island effect, driven by global climate warming, urbanization, and industrialization, has led to a sharp rise in the number of people exposed to extreme heat conditions. According to the World Health Organization (WHO), extreme heat events pose a major threat to both environmental and occupational health, emerging as the leading cause of weather-related mortality. Epidemiological data indicate that heat exposure caused approximately 489,000 deaths annually between 2000 and 2019, with Asia accounting for 45% and Europe for 36% of these fatalities. Notably, heat-related deaths in adults aged 65 and above rose by roughly 85% when the periods 2000–2004 and 2017–2021 were compared (Liu et al. 2021b). Against the backdrop of these worrying trends, it is urgent to conduct research on extreme heat’s health effects and develop effective prevention measures (Faurie et al. 2022). Oxidation-reduction reactions are essential for sustaining life. Oxygen, nitrogen, and sulfur compounds play a pivotal role in regulating redox balance, which governs a wide array of critical cellular functions. Under normal physiological conditions, cells generate reactive oxygen species (ROS) and reactive nitrogen species (RNS) as metabolic byproducts to maintain homeostasis (Lennicke and Cochemé 2021). However, exposure to high-temperature environments can disrupt this equilibrium, leading to oxidative or reductive stress (Slimen et al. 2014). Emerging evidence suggests that heat stress triggers excessive ROS accumulation, resulting in deleterious changes to cellular structure and function (Rahman and Rahman 2021, Hendrix et al. 2023). As an illustration, heat stress induces higher mitochondrial ROS levels in sperm, thereby causing oxidative DNA damage and DNA single-strand breaks (SSBs) in the germ cells of men (Habibi et al. 2022). In the human body, nitric oxide (NO) is essential for normal physiological functions and is involved in the development of various diseases (Stichtenoth and Frölich 1998, Mapp et al. 2001, Paravicini and Touyz 2006). When RNS are dysregulated, the primary molecules involved are NO and its derivatives—such as peroxynitrite (ONOO⁻). Studies have further shown that heat stress can upregulate the activity of nitric oxide synthase (NOS), leading to increased NO concentrations in plant cells (Yuan et al. 2024). Moreover, the elevated malondialdehyde (MDA) levels observed across multiple tissues in spotted seabass exposed to 35°C water temperature suggest that MDA content may serve as a reliable biomarker for heat stress evaluation in this species (Yang et al. 2024a). Metabolomic analyses of Qinling lenok trout have shown that heat stress significantly reduces the abundance of glutathione, which is synthesized from glutamate and glycine (Fang et al. 2023). Despite these findings, significant knowledge gaps remain regarding how high-temperature environments affect redox homeostasis in humans. A total of 330 volunteers were enrolled and assessed twice: once in March (moderate spring temperatures) and again in August (peak summer heat). During each assessment, we collected serum samples from participants to analyze redox status indicators. Our findings reveal that elevated temperatures affect redox balance in healthy individuals and identify several potential markers. These insights could inform the development of preventive therapies for heat-related disorders. 2. Experimental methodology 2.1. Subject recruitment The study recruited 330 healthy volunteers from the Hubei Provincial Center for Disease Control and Prevention (Wuhan, Hubei, China). After excluding 15 participants—due to inadequate sample quantities required for analyzing the six markers—the final cohort consisted of 315 subjects. Blood samples were obtained from these 315 subjects to assess each participant’s redox status, and every individual provided informed consent prior to their involvement in the research. 2.2. Serum sampling Protocols for blood collection, pretreatment, and storage were implemented in accordance with methods previously reported in the literature (Wang et al. 2021). Participants underwent an overnight fast (with no beverages allowed except a small volume of water) before blood was collected from 8:00 to 10:00 AM the next morning. Blood samples were drawn into plastic vacutainers without anticoagulants. Tubes holding the whole blood were left undisturbed on a laboratory bench for 50 minutes; subsequent serum isolation was performed via centrifugation at 4000 × g for 5 minutes at room temperature (RT). Immediately after serum separation, samples were either placed on dry ice for prompt analysis of analytes (FRAA, folate, VB12, and ferritin) or stored at -80 °C for later testing. 2.3. Measurement of serum nitrite and nitrate For the quantification of nitrite and nitrate in biological fluids, chemiluminescence analysis has gained broad recognition as a reliable approach, as documented in prior research (Baksu et al. 2005, MacArthur et al. 2007, Wang et al. 2021). By utilizing the highly sensitive ozone-chemiluminescence method, this analytical technique achieves a detection sensitivity of 1 pmol for liquid samples—equivalent to 1 nM when the injection volume is 1 mL. For the determination of nitrite and nitrate concentrations in the present study, a Nitric Oxide Analyzer 280i (NOA 280i; GE) was used. The reducing reagents applied were as follows: triiodide ion for nitrite detection, and a vanadium trichloride (VCl₃) solution supplemented with 1 M hydrochloric acid (HCl) for nitrate quantification. Standard solutions were injected in duplicate into the purge vessel of the NOA. Daily calibration of the NOA instrument was deemed successful only when calibration curves yielded a coefficient of determination (R²) > 0.999. To evaluate the recovery ratio and relative standard deviation (RSD), comparisons among the three pretreatment methods were conducted by injecting each sample six times. Each serum sample was analyzed immediately after pretreatment, and samples were kept on ice throughout the measurement process to maintain stability. Additionally, all assays for samples in each group were performed in triplicate to ensure result reliability. 2.4. Measurement of serum GSSG High-performance liquid chromatography (HPLC) was employed to analyze the glutathione forms in human erythrocytes, using o-phthalaldehyde (OPA) and N-acetyl-cysteine ethyl ester as reagents. This analytical procedure followed the method originally developed by Jan and Sabine (Michaelsen et al. 2009). A C18 column (250 mm × 4.6 mm, 5 μm) was employed for detection, with the mobile phase composed of 20 mM methanol/phosphate buffer adjusted to pH 6.0. Separation was achieved via HPLC (Thermo U3000) at 30 °C and a constant flow rate of 1 mL/min, and the eluent was monitored using an excitation wavelength of 350 nm and an emission wavelength of 420 nm. To generate a standard curve, the concentration of the standard solution (0.01–5 μM) was plotted on the horizontal axis, with the corresponding peak area on the vertical axis. Calculations were performed by referencing sample peak areas to the standard curve, and results were expressed in μmol/L. Further dilution was needed if the measured GSSG values fell outside the linear range of the standard curve. 2.5. Measurement of serum MDA MDA was quantified using the thio barbituric acid (TBA) method. In this procedure, the sample is heated at high temperatures under acidic conditions, causing MDA to react with TBA to form a MDA-(TBA)₂ compound, which exhibits maximum absorption at 532 nm (Templar et al. 1999, Bastos et al. 2012). A C18 column (250 mm × 4.6 mm, 5 μm) was used for the analysis, with the mobile phase composed of 25 mM phosphate buffer (pH 6.5) and methanol in a 40:60 (v/v) ratio. HPLC (Thermo U3000) was employed for separation at 32 °C, with a constant flow rate of 1 mL/min, and the eluent was monitored at a wavelength of 532 nm. To generate a standard curve, the concentrations of the standard solution (0.01–5 μM) were set as the x-axis, and the corresponding peak areas as the y-axis. Calculations were done by comparing sample peak areas to the standard curve, and results were expressed in μmol/L. Additional dilution was required if the measured MDA values fell outside the linear range of the standard curve. 2.6. Ferric reducing ability of serum This study adopted the ferric reducing ability of plasma (FRAP) assay, which was first developed by Benzie and Strain (Benzie and Strain 1996, Pulido et al. 2000, Fejes et al. 2024). Different concentrations of Fe²⁺ standard solutions were prepared using ferrous sulfate as the standard. The working solution was mixed with either the standard solution or the sample in a ratio of 30:1 and incubated at room temperature for 6 minutes, protected from light. Following incubation, the absorbance was measured at 593 nm using UV detection (Carry 60). The absorbance values of the standard solutions were plotted to create a standard curve, and the absorbance of the samples was compared to this curve to calculate their antioxidant capacity. 2.7. ABTS assay 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) is a chemical compound capable of generating a radical cation. The ABTS assay stands out as one of the simplest and most reliable methods for analyzing the antioxidant activity of substances like albumin— a molecule that denatures in methanol solutions and under low pH conditions. In this study, the ABTS assay was performed following modified protocols originally described by Re et al. and Ilyasov et al (Re et al. 1999, Ilyasov et al. 2020). For assay preparation, a 7 mM ABTS solution and a 2.45 mM potassium persulfate solution were mixed at a 1:1 volume ratio, and the mixture was incubated overnight to facilitate reaction completion. Ascorbic acid was employed as the reference standard, with which a set of standard solutions with gradient concentrations was prepared. Following this, the ABTS working solution was combined with either the standard solution or the sample at a 200:1 volume ratio; the resulting mixture was then incubated at room temperature for 6 minutes under light-protected conditions. After incubation, absorbance is measured at either 414–417 nm or 730–734 nm. The absorbance of the standard solution detected at 732 nm is plotted to create a standard curve, and the absorbance of the samples is compared to this curve to calculate their antioxidant capacity. 2.8. Statistical analysis A two-tailed unpaired Student’s t -test was applied to compare differences among groups and subgroups, with OriginPro 9.1 (OriginLab) and Prism 7 (GraphPad) serving as the data analysis tools. Meanwhile, SPSS Statistics V22.0 (IBM) was utilized to carry out correlation analysis, multivariate analysis of variance (MANOVA), and independent t -test. All measurement values following parametric distributions were expressed as mean ± standard deviation (mean ± SD). The criteria for statistical significance were set as follows: a p -value < 0.05 indicated a statistically significant difference, and a p -value between 0.05 and 0.1 was regarded as a borderline significant difference. All experimental procedures were repeated independently for at least three times to confirm the stability of the results. 3. Results 3.1. Subjects recruited for this study This study enrolled 330 healthy volunteers from four distinct regions in Hubei Province; after excluding 15 participants due to insufficient sample volumes for six-marker analysis, the final cohort comprised 315 individuals (114 males, 201 females). The regional distribution of included participants was as follows: Xiaogan (n=90; 32 males, 58 females), Huangshi (n=47; 8 males, 39 females), Qianjiang (n=80; 28 males, 52 females), and Gongan (n=98; 46 males, 52 females) (Table 1). Participants underwent two follow-up visits during March (spring) and August (summer) of the study year. Complete demographic characteristics are presented in Table 1. In order to ensure that the sampling area has no significant effect on the experimental results, we conducted separate analysis of redox indicators for samples from different sampling areas to reveal the differences. At each visit, fasting serum samples were collected for comprehensive redox status assessment. All participants provided written informed consent prior to enrollment, in compliance with the institutional ethical requirements. 3.2. Heat Stress and Oxidative Dysregulation in Volunteers Prolonged exposure to high temperatures, both during the day and at night, induces cumulative physiological stress that disrupts redox homeostasis and increases the risk of heat-related health complications and mortality (Li et al. 2025). Epidemiological and clinical studies have established that heat stress disorders are associated with significant alterations in oxidative stress markers (Yang et al. 2024b). To systematically evaluate the association between environmental heat exposure and systemic redox imbalance, we conducted a comparative analysis of two distinct seasonal periods in Hubei Province, China: a thermoneutral reference period (March, mean temperature: 15.2 ± 3.1°C) and a peak heat stress period (August, mean temperature: 31.6 ± 2.8°C) (Table S1). A heat wave is defined as a prolonged meteorological event characterized by sustained abnormally high temperatures that exceed regional adaptation capacities, resulting in significant impacts on human health, ecosystems, and infrastructure. The World Meteorological Organization recommends that the weather process with a daily maximum temperature higher than 32℃ and lasting more than 3 days is called a heat wave (C.H. HUANG Zhuo 2011). Our data showed that none of the four cities surveyed experienced a heat wave in the month of March in spring (Figures 1A-D). In contrast, in August, Gong 'an, Qianjiang and Xiaogan experienced 23 days of high-temperature heatwave conditions (Figures 1E-G), accounting for 74.2% of the total number of days in the month; Huangshi experienced 26 days of high-temperature heatwave conditions., accounting for 83.9% of the total number of days in the month (Figure 1F). Subsequently, serum samples collected from the recruited volunteers in March and August were analyzed for six redox markers. Our findings revealed that serum nitrite levels and GSSG concentrations exhibited progressive declines of 21.01% and 5.63%, respectively, during the August heat period compared to measurements taken in March (Figures 2A, C). In contrast, we observed a 14.35% increase in MDA (Table 2; Figure 2D) concentrations and a 6.16% rise in T-AOC (Table 2; Figures 2E-F). We also collected serum samples from the same volunteer cohort in April, a period with temperatures similar to those observed in March (Figures S1A-D). Under conditions where the temperature has risen significantly but there is no high-temperature heatwave, the redox status of serum samples collected in April remained relatively stable (Figures S2A–F). These results indicated that nitrite, oxidation and reduction indices were affected to some extent by heat stress, with MDA and T-AOC 2 showing the most significant changes. Notably, the observed biochemical changes occurred in the absence of clinical manifestations, suggesting that even ostensibly healthy individuals experience significant redox perturbations under heat stress. 3.3. Heat stress induces sex-divergent redox responses Emerging evidence from mammalian studies suggested that females exhibit superior ROS buffering capacity compared to males (Tiberi et al. 2023). To investigate sex-specific differences in oxidative stress responses under heat challenge, we performed stratified analysis of redox markers in our cohort under seasonal temperature variations. Our findings revealed differential heat sensitivity in lipid peroxidation and antioxidant responses between sexes (Figure 3). Baseline levels of nitrate, nitrite, and GSSG showed no significant sex differences (all p > 0.05; Figures 3A-C). However, high-temperature exposure induced sex-divergent patterns in MDA and antioxidant capacity (Figure 3D). Notably, the T-AOC 2 was higher in males under thermoneutral conditions (March: male vs female, p =0.012), but this difference disappeared under extreme heat (August: p = 0.38). The increase of T-AOC 2 in females after heat stress is the main factor responsible for the increase of T-AOC 2 levels (Figure 2F）, while there is no significant change in T-AOC 2 levels in males after heat stress. These results suggest that heat stress amplifies inherent sexual dimorphism in oxidative homeostasis. After excluding the influence of sex as much as possible, there was no doubt that MDA and nitrite levels showed significant changes under heat stress and were minimally affected by sex (Table S2). 3.4. Age-Independent Oxidative Stress Response to Heat Exposure While cumulative evidence indicates that oxidative stress increases with age, and multiple redox parameters are established markers of aging (El Assar et al. 2024), the interaction between chronological aging and heat-induced oxidative damage in healthy populations remains poorly characterized. To fill this knowledge gap, we conducted stratified analyses of serum redox markers in four age quartiles (20-30 years, 30-40 years, 40-50 years, and 50-60 years) under different seasonal heat environments. The results showed that serum nitrate levels were positively correlated with age (r < 0.5), and this correlation persisted across time, sex, and temperature conditions – older individuals generally exhibited higher nitrate concentrations (Liguori et al. 2018) (Figures 4B, H). Notably, MDA levels were not significantly correlated with age in March when temperatures were relatively mild (Figure 4D), but showed a significant positive correlation ( p < 0.01) in August under high-temperature conditions (Figure 4J). In contrast, T-AOC 1 was weakly positively correlated with age ( p < 0.05) in March under mild temperatures, but this association disappeared in August (Figures 4E, K). Furthermore, serum levels of nitrite, GSSG, and T-AOC 2 in healthy individuals did not show significant associations with age in either March or August (Figures 4A, C, F). These results suggest that older individuals are more sensitive to heat-induced oxidative damage, with this vulnerability being most evident in the elevated MDA levels. 4. Discussion The increasing frequency and intensity of extreme heat events due to global climate change, urbanization, and the urban heat island effect have significantly elevated public health risks. Understanding the physiological responses to environmental heat stress is important for developing early warning systems and preventive strategies. In this study, we conducted a longitudinal cohort analysis involving 330 healthy individuals from four cities in Hubei Province, China, to investigate the impact of high-temperature exposure on serum redox homeostasis. Our findings demonstrated that environmental heat stress affected the body’s oxidative balance, as evidenced by reduced nitric oxide bioavailability, increased lipid peroxidation, and altered antioxidant defense capacity. Importantly, we identified MDA as a potentially sensitive and robust marker of heat-induced oxidative damage. MDA is the core product of lipid peroxidation, and many studies have demonstrated the correlation between MDA and heat stress (Liu et al. 2021a). In plants, heat stress catalyzes the peroxidation of polyunsaturated fatty acids such as linolenic acid by activating enzymatic reactions such as lipoxygenase (LOX) to generate lipid hydroperoxides (LOOH), which are further decomposed to produce MDA. For example, in Arabidopsis and spinach, heat stress-induced MDA is mainly derived from the peroxidation of chloroplast membrane lipids (Kumar et al. 2023). In animal models, heat stress leads to mitochondrial dysfunction and free radical burst, accelerating MDA production through non-enzymatic pathways. For example, MDA content was significantly increased in the thigh muscle of heat-stressed broilers and was inversely correlated with decreased antioxidant enzyme activity (Ramiah et al. 2019). MDA production is also associated with heat stress-induced ferroptosis in mammals. In the mouse model of exhaustion heat stroke, heat stress up-regulates ACSL4 expression through the YAP/TEAD pathway, which promotes the insertion of arachidonic acid into phospholipids, exacerbates lipid peroxidation and MDA accumulation (He et al. 2022). This suggests that MDA accumulation is not only a consequence of oxidative damage but also a key link in heat stress-triggered programmed cell death, such as ferroptosis. Our data revealed marked sexual dimorphism in the redox response to heat exposure. Male participants exhibited significantly higher MDA accumulation compared to females, suggesting greater susceptibility to lipid peroxidation under heat stress. Conversely, females demonstrated superior T-AOC 2 resilience, potentially reflecting protective effects conferred by estrogen-mediated antioxidant pathways. These findings show that sex differences may play a crucial role in the physiological response to environmental stressors. We also examined how age influences redox adaptations to heat stress. While baseline nitrate levels increased linearly with advancing age—a trend likely attributable to reduced endothelial nitric oxide synthase (eNOS) activity—this age-related increase was not further exacerbated by acute heat exposure. However, older individuals showed a more pronounced increase in MDA levels following heat stress, highlighting their heightened vulnerability to oxidative damage. Gongan, Qianjiang and Xiaogan experienced 23 days of high-temperature heatwave conditions. These findings suggest that while aging primarily affects basal redox regulation, it potentiates the harmful effects of environmental stressors such as heat. Although extensive epidemiological investigations have documented heat-related changes in oxidative stress, there is an ongoing knowledge gap regarding the temporal dynamics of redox homeostasis in healthy populations, especially the possibility of MDA through longitudinal serum marker analysis before and after heat exposure. This study provides fundamental insights into the effects of high temperature environments on redox balance in healthy individuals. On this basis, we propose that MDA—identified as a key marker in this study and a core product of heat stress-induced lipid peroxidation—has the potential to serve as a biomarker for heat stress. In the future, it is necessary to further clarify its dynamic threshold, spatial-temporal distribution in different species, and its association with early stress signals to improve its utility as early warning markers. This will help to pay attention to population health in hot environment, provide important data for assessing the impact of high temperature on population health, and provide scientific basis for the development of public health policies. Declarations Author Contributions Conceptualization: Hong Zhou, Jiachen Dai, and Jun Wang; Methodology: Hong Zhou and Jiachen Dai; Validation: Yuemei Zhang, Wenwen Tang, Jiachen Dai and Yingying Ren; Formal Analysis: Xinming Zhang and Hong Zhou; Investigation: Hong Zhou, Jiachen Dai; Resources, George H. Lorimer, Hasan Bayram, Reza A Ghilad, Fanghua Mei and Junqiang Xu; Data Curation, Yuemei Zhang, Jiachen Dai and Hong Zhou; Writing – Original Draft Preparation, Hong Zhou and Jiachen Dai; Writing – Review & Editing, Hong Zhou, Jiachen Dai and Jun Wang; Funding Acquisition, Hong Zhou and Jun Wang. Funding This work was funded by the Collaborative Grant-in-Aid of the HBUT National "111" Center for Cellular Regulation and Molecular Pharmaceutics (XBTK-2024001) (Hong Zhou), and Hubei Provincial Department of Education Innovation Group Grant (4115/00257) (Jun Wang). Institutional Review Board Statement The study was conducted in accordance with the Declaration of Helsinki, and approved by the Hubei Center for Disease Control and Prevention, Wuhan, People’s Republic of China (Approval Number: 2022-023-02, approved on 8 July 2022). Informed Consent Statement Informed consent was obtained from all subjects involved in this study. Data Availability Statement The raw data supporting the conclusions of this article will be made available by the authors on request. Conflicts of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References 2023. 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Liu. 2024b. Effects of isorhamnetin on liver injury in heat stroke-affected rats under dry-heat environments via oxidative stress and inflammatory response. Sci Rep 14: 7476. Yuan, Y., M. Tan, M. Zhou, M. J. Hassan, L. Lin, J. Lin, Y. Zhang, and Z. Li. 2024. Drought priming-induced stress memory improves subsequent drought or heat tolerance via activation of γ-aminobutyric acid-regulated pathways in creeping bentgrass. Plant Biol (Stuttg). Tables Table 1. Demographic characteristics of volunteers Xiaogan City （ n=90 ） Huangshi City （ n=47 ） Qianjiang City （ n=80 ） Gongan City （ n=98 ） sex (n, M/F) 32/58 8/39 28/52 46/52 Age (mean ± SD) Age (years old) 40±9 23 ~ 56 38±9 20 ~ 57 39±9 22 ~ 59 38±9 22 ~ 58 Table 2. Summary of redox markers at sampling time redox marker s Concentration (March) Concentration (August) Nitrate 4.035 ~ 168.5 µM 5.385 ~ 252 µM Nitrite 0.1112 ~ 3.947 µM 0.1218 ~ 2.886 µM GSSG 0.02980 ~ 2.563 µM 0.0337 ~ 1.134 µM MDA 0.0192 ~ 0.1896 µM 0.0054 ~ 0.2718 µM T-AOC 1 0.0192 ~ 0.1896 mM 0.4478 ~ 1.651 mM T-AOC 2 0.4462 ~ 2.146 mM 1.775 ~ 4.298 mM Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7350440","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511201013,"identity":"1bf09939-ec67-48c5-83bc-f9c694ea9743","order_by":0,"name":"Hong Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYDACCQYGZhDNL8ED5jM2EK1FcgZQywGStBjcIFaL/OzmY9IFFXfsNt/uPSb9gcFGdsMB5mcP8GlhnHMsTXrGmWfJ2+6cS5M4wJBmvOEAm7kBPi3MEjlm0rxth5PNbuSYAbUcTtxwgIdNAp8WNpgW4xlgLf8Ja+GBarEzkABrOUBYi4REWrI1z5nDCRI38pItzhgkG888zGaGV4v8jOSDt3kqDtvzz8g9eKOiwk6273jzM7xaYCCxAUyBgoqZGPVAYE+kulEwCkbBKBiJAAAtgEb/f8L2aQAAAABJRU5ErkJggg==","orcid":"","institution":"Hubei University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Hong","middleName":"","lastName":"Zhou","suffix":""},{"id":511201014,"identity":"d25555c2-0675-4b85-8c64-aef0801e7310","order_by":1,"name":"Jiachen Dai","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jiachen","middleName":"","lastName":"Dai","suffix":""},{"id":511201015,"identity":"bf1ba778-213f-41aa-904e-2fc2ca7115d7","order_by":2,"name":"Yuemei Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yuemei","middleName":"","lastName":"Zhang","suffix":""},{"id":511201016,"identity":"46d57174-3556-4e7a-8aec-e99075190894","order_by":3,"name":"Wenwen Tang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wenwen","middleName":"","lastName":"Tang","suffix":""},{"id":511201017,"identity":"3a38e99c-62f3-4944-98ec-4d519dddc9eb","order_by":4,"name":"Yingying Ren","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yingying","middleName":"","lastName":"Ren","suffix":""},{"id":511201018,"identity":"a2e76d63-714b-4ebe-9b0e-f2f5ad8c848f","order_by":5,"name":"Xinming Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xinming","middleName":"","lastName":"Zhang","suffix":""},{"id":511201019,"identity":"50bef993-4e63-4d71-9de1-4b6fdf414b51","order_by":6,"name":"George H. Lorimer","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"George","middleName":"H.","lastName":"Lorimer","suffix":""},{"id":511201020,"identity":"b429cba7-c646-4682-89c2-f43da227201f","order_by":7,"name":"Hasan Bayram","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hasan","middleName":"","lastName":"Bayram","suffix":""},{"id":511201021,"identity":"df774f3f-4f35-420e-943c-26bc6ab86c33","order_by":8,"name":"Reza A Ghilad","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Reza","middleName":"A","lastName":"Ghilad","suffix":""},{"id":511201022,"identity":"cd9103f2-70c7-48f1-83c6-82f661ca2a24","order_by":9,"name":"Junqiang Xu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Junqiang","middleName":"","lastName":"Xu","suffix":""},{"id":511201023,"identity":"0f4d10a4-d9d9-4238-a9e5-5753373a2e06","order_by":10,"name":"Fanghua Mei","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Fanghua","middleName":"","lastName":"Mei","suffix":""},{"id":511201024,"identity":"c458cd24-ac26-4bb2-94b6-d15bc9e5411c","order_by":11,"name":"Jun Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-08-12 02:19:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7350440/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7350440/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91121224,"identity":"e2304a27-85aa-434f-b748-f5ee9d5887cd","added_by":"auto","created_at":"2025-09-11 19:08:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":448764,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRecorded temperature every day in March or August of 2023. (A, B)\u003c/strong\u003e Recorded temperature in Gongan in March (A) and August (B) 2023. \u003cstrong\u003e(C, D)\u003c/strong\u003e Recorded temperature in Huangshi in March (C) and August (D) 2023.\u003cstrong\u003e (E, F)\u003c/strong\u003e Recorded temperature in Qianjiang in March (E) and August (F) 2023.\u003cstrong\u003e (G, H)\u003c/strong\u003e Recorded temperature in Xiaogan in March (G) and August (H) 2023.\u003c/p\u003e","description":"","filename":"Picture1.png","url":"https://assets-eu.researchsquare.com/files/rs-7350440/v1/5b7083c709fb5187d7eb6adc.png"},{"id":91122926,"identity":"257746cc-4dbf-4722-ab3f-03514a8131ab","added_by":"auto","created_at":"2025-09-11 19:24:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":252265,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of high-temperature exposure on serum redox markers in volunteers. (A) \u003c/strong\u003eSerum nitrite levels were significantly lower in August compared to March. \u003cstrong\u003e(B) \u003c/strong\u003eNo significant difference in serum nitrate levels was observed between March and August. \u003cstrong\u003e(C) \u003c/strong\u003eMDA levels were significantly increased in August compared to March. \u003cstrong\u003e(D) \u003c/strong\u003eGSSG levels were significantly lower in August compared to March. \u003cstrong\u003e(E)\u003c/strong\u003e T-AOC\u003csub\u003e1\u003c/sub\u003e levels were significantly increased in August compared to March. \u003cstrong\u003e(F) \u003c/strong\u003eT-AOC\u003csub\u003e2\u003c/sub\u003e levels were significantly increased in August compared to March. Data are presented as violin plots with median and interquartile range. Statistical significance: ****\u003cem\u003ep\u003c/em\u003e ≤ 0.0001; ***\u003cem\u003ep\u003c/em\u003e ≤ 0.001; **\u003cem\u003ep\u003c/em\u003e ≤ 0.01; *\u003cem\u003ep\u003c/em\u003e ≤ 0.05; ns: not significant.\u003c/p\u003e","description":"","filename":"Picture2.png","url":"https://assets-eu.researchsquare.com/files/rs-7350440/v1/e6e9a66d279f6eebd7e082f4.png"},{"id":91122277,"identity":"bc33691e-9040-42a7-987c-355fdab4eaa2","added_by":"auto","created_at":"2025-09-11 19:16:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":292205,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of high-temperature exposure on serum redox markers in different sexs. (A) \u003c/strong\u003eSerum nitrite (NO₂⁻) levels were significantly lower in both male and female subjects in August compared to March. (B) No significant difference was observed in serum nitrate (NO₃⁻) levels among different groups. (C) GSSG levels showed no significant variation between groups. (D) MDA levels were significantly reduced in males in August compared to March, with a moderate decrease in females. (E) T-AOC₁ was significantly higher in March females compared to March males, and August males showed a significant increase compared to March males. (F) T-AOC₂ was significantly higher in March males compared to March females, with a significant increase in August males compared to March males. Data are presented as violin plots with median and interquartile range. Statistical significance: ****\u003cem\u003ep\u003c/em\u003e≤ 0.0001; ***\u003cem\u003ep\u003c/em\u003e ≤ 0.001; **\u003cem\u003ep\u003c/em\u003e ≤ 0.01; *\u003cem\u003ep\u003c/em\u003e ≤ 0.05; ns: not significant.\u003c/p\u003e","description":"","filename":"Picture3.png","url":"https://assets-eu.researchsquare.com/files/rs-7350440/v1/59486ae6dc6908b7cf3062b8.png"},{"id":91121226,"identity":"48672205-d33a-429a-bc69-e69d1c3408d1","added_by":"auto","created_at":"2025-09-11 19:08:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":635274,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between serum redox markers and age in volunteers. (A-F\u003c/strong\u003e) Correlation between serum redox markers and age in March, including nitrite (A), nitrate (B), GSSG (C), MDA (D), T-AOC₁ (E), and T-AOC₂ (F). (\u003cstrong\u003eG-L\u003c/strong\u003e) Correlation between serum redox markers and age in August, including nitrite (G), nitrate (H), GSSG (I), MDA (J), T-AOC₁ (K), and T-AOC₂ (L). Pearson correlation coefficients (r) and p-values are provided for the overall population, as well as for males and females separately. Significant correlations (\u003cem\u003ep\u003c/em\u003e ≤ 0.05) are highlighted.\u003c/p\u003e","description":"","filename":"Picture4.png","url":"https://assets-eu.researchsquare.com/files/rs-7350440/v1/0e67b7274279bb1bc11190be.png"},{"id":95523599,"identity":"0dab7a60-a5b0-4da4-b6b8-49d51d3a2fad","added_by":"auto","created_at":"2025-11-10 09:59:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3173795,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7350440/v1/85a3eab3-f6f9-490d-baa9-c251c1b95a9d.pdf"},{"id":91121235,"identity":"070c0195-cffc-4197-b1b1-2dc4388dcce8","added_by":"auto","created_at":"2025-09-11 19:08:48","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":333285,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7350440/v1/c0cf11745e2f8c6c1e9c0833.docx"}],"financialInterests":"","formattedTitle":"Environmental Heat Stress Modulates Systemic Redox Homeostasis: A Longitudinal marker Analysis in Healthy Adults","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe intensification of the urban heat island effect, driven by global climate warming, urbanization, and industrialization, has led to a sharp rise in the number of people exposed to extreme heat conditions. According to the World Health Organization (WHO), extreme heat events pose a major threat to both environmental and occupational health, emerging as the leading cause of weather-related mortality. Epidemiological data indicate that heat exposure caused approximately 489,000 deaths annually between 2000 and 2019, with Asia accounting for 45% and Europe for 36% of these fatalities. Notably, heat-related deaths in adults aged 65 and above rose by roughly 85% when the periods 2000–2004 and 2017–2021 were compared\u0026nbsp;(Liu et al. 2021b). Against the backdrop of these worrying trends, it is urgent to conduct research on extreme heat’s health effects and develop effective prevention measures\u0026nbsp;(Faurie et al. 2022).\u003c/p\u003e\n\u003cp\u003eOxidation-reduction reactions are essential for sustaining life. Oxygen, nitrogen, and sulfur compounds play a pivotal role in regulating redox balance, which governs a wide array of critical cellular functions. Under normal physiological conditions, cells generate reactive oxygen species (ROS) and reactive nitrogen species (RNS) as metabolic byproducts to maintain homeostasis (Lennicke and Cochemé 2021). However, exposure to high-temperature environments can disrupt this equilibrium, leading to oxidative or reductive stress (Slimen et al. 2014). Emerging evidence suggests that heat stress triggers excessive ROS accumulation, resulting in deleterious changes to cellular structure and function (Rahman and Rahman 2021, Hendrix et al. 2023). As an illustration, heat stress induces higher mitochondrial ROS levels in sperm, thereby causing oxidative DNA damage and DNA single-strand breaks (SSBs) in the germ cells of men (Habibi et al. 2022). In the human body, nitric oxide (NO) is essential for normal physiological functions and is involved in the development of various diseases (Stichtenoth and Frölich 1998, Mapp et al. 2001, Paravicini and Touyz 2006). When RNS are dysregulated, the primary molecules involved are NO and its derivatives—such as peroxynitrite (ONOO⁻). Studies have further shown that heat stress can upregulate the activity of nitric oxide synthase (NOS), leading to increased NO concentrations in plant cells (Yuan et al. 2024). Moreover, the elevated malondialdehyde (MDA) levels observed across multiple tissues in spotted seabass exposed to 35°C water temperature suggest that MDA content may serve as a reliable biomarker for heat stress evaluation in this species (Yang et al. 2024a). Metabolomic analyses of Qinling lenok trout have shown that heat stress significantly reduces the abundance of glutathione, which is synthesized from glutamate and glycine (Fang et al. 2023). Despite these findings, significant knowledge gaps remain regarding how high-temperature environments affect redox homeostasis in humans.\u003c/p\u003e\n\u003cp\u003eA total of 330 volunteers were enrolled and assessed twice: once in March (moderate spring temperatures) and again in August (peak summer heat). During each assessment, we collected serum samples from participants to analyze redox status indicators. Our findings reveal that elevated temperatures affect redox balance in healthy individuals and identify several potential markers. These insights could inform the development of preventive therapies for heat-related disorders.\u003c/p\u003e"},{"header":"2. Experimental methodology","content":"\u003cp\u003e2.1. Subject recruitment\u003c/p\u003e\n\u003cp\u003eThe study recruited 330 healthy volunteers from the Hubei Provincial Center for Disease Control and Prevention (Wuhan, Hubei, China). After excluding 15 participants—due to inadequate sample quantities required for analyzing the six markers—the final cohort consisted of 315 subjects. Blood samples were obtained from these 315 subjects to assess each participant’s redox status, and every individual provided informed consent prior to their involvement in the research.\u003c/p\u003e\n\u003cp\u003e2.2. Serum sampling\u003c/p\u003e\n\u003cp\u003eProtocols for blood collection, pretreatment, and storage were implemented in accordance with methods previously reported in the literature (Wang et al. 2021). Participants underwent an overnight fast (with no beverages allowed except a small volume of water) before blood was collected from 8:00 to 10:00 AM the next morning. Blood samples were drawn into plastic vacutainers without anticoagulants. Tubes holding the whole blood were left undisturbed on a laboratory bench for 50 minutes; subsequent serum isolation was performed via centrifugation at 4000 × g for 5 minutes at room temperature (RT). Immediately after serum separation, samples were either placed on dry ice for prompt analysis of analytes (FRAA, folate, VB12, and ferritin) or stored at -80 °C for later testing.\u003c/p\u003e\n\u003cp\u003e2.3. Measurement of serum nitrite and nitrate\u003c/p\u003e\n\u003cp\u003eFor the quantification of nitrite and nitrate in biological fluids, chemiluminescence analysis has gained broad recognition as a reliable approach, as documented in prior research (Baksu et al. 2005, MacArthur et al. 2007, Wang et al. 2021). By utilizing the highly sensitive ozone-chemiluminescence method, this analytical technique achieves a detection sensitivity of 1 pmol for liquid samples—equivalent to 1 nM when the injection volume is 1 mL. For the determination of nitrite and nitrate concentrations in the present study, a Nitric Oxide Analyzer 280i (NOA 280i; GE) was used. The reducing reagents applied were as follows: triiodide ion for nitrite detection, and a vanadium trichloride (VCl₃) solution supplemented with 1 M hydrochloric acid (HCl) for nitrate quantification. Standard solutions were injected in duplicate into the purge vessel of the NOA. Daily calibration of the NOA instrument was deemed successful only when calibration curves yielded a coefficient of determination (R²) \u0026gt; 0.999.\u003c/p\u003e\n\u003cp\u003eTo evaluate the recovery ratio and relative standard deviation (RSD), comparisons among the three pretreatment methods were conducted by injecting each sample six times. Each serum sample was analyzed immediately after pretreatment, and samples were kept on ice throughout the measurement process to maintain stability. Additionally, all assays for samples in each group were performed in triplicate to ensure result reliability.\u003c/p\u003e\n\u003cp\u003e2.4. Measurement of serum GSSG\u003c/p\u003e\n\u003cp\u003eHigh-performance liquid chromatography (HPLC) was employed to analyze the glutathione forms in human erythrocytes, using o-phthalaldehyde (OPA) and N-acetyl-cysteine ethyl ester as reagents. This analytical procedure followed the method originally developed by Jan and Sabine (Michaelsen et al. 2009). A C18 column (250 mm × 4.6 mm, 5 μm) was employed for detection, with the mobile phase composed of 20 mM methanol/phosphate buffer adjusted to pH 6.0. Separation was achieved via HPLC (Thermo U3000) at 30 °C and a constant flow rate of 1 mL/min, and the eluent was monitored using an excitation wavelength of 350 nm and an emission wavelength of 420 nm. To generate a standard curve, the concentration of the standard solution (0.01–5 μM) was plotted on the horizontal axis, with the corresponding peak area on the vertical axis. Calculations were performed by referencing sample peak areas to the standard curve, and results were expressed in μmol/L. Further dilution was needed if the measured GSSG values fell outside the linear range of the standard curve.\u003c/p\u003e\n\u003cp\u003e2.5. Measurement of serum MDA\u003c/p\u003e\n\u003cp\u003eMDA was quantified using the thio barbituric acid (TBA) method. In this procedure, the sample is heated at high temperatures under acidic conditions, causing MDA to react with TBA to form a MDA-(TBA)₂ compound, which exhibits maximum absorption at 532 nm\u0026nbsp;(Templar et al. 1999, Bastos et al. 2012). A C18 column (250 mm × 4.6 mm, 5 μm) was used for the analysis, with the mobile phase composed of 25 mM phosphate buffer (pH 6.5) and methanol in a 40:60 (v/v) ratio. HPLC (Thermo U3000) was employed for separation at 32 °C, with a constant flow rate of 1 mL/min, and the eluent was monitored at a wavelength of 532 nm. To generate a standard curve, the concentrations of the standard solution (0.01–5 μM) were set as the x-axis, and the corresponding peak areas as the y-axis. Calculations were done by comparing sample peak areas to the standard curve, and results were expressed in μmol/L. Additional dilution was required if the measured MDA values fell outside the linear range of the standard curve.\u003c/p\u003e\n\u003cp\u003e2.6. Ferric reducing ability of serum\u003c/p\u003e\n\u003cp\u003eThis study adopted the ferric reducing ability of plasma (FRAP) assay, which was first developed by Benzie and Strain (Benzie and Strain 1996, Pulido et al. 2000, Fejes et al. 2024). Different concentrations of Fe²⁺ standard solutions were prepared using ferrous sulfate as the standard. The working solution was mixed with either the standard solution or the sample in a ratio of 30:1 and incubated at room temperature for 6 minutes, protected from light. Following incubation, the absorbance was measured at 593 nm using UV detection (Carry 60). The absorbance values of the standard solutions were plotted to create a standard curve, and the absorbance of the samples was compared to this curve to calculate their antioxidant capacity.\u003c/p\u003e\n\u003cp\u003e2.7. ABTS assay\u003c/p\u003e\n\u003cp\u003e2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) is a chemical compound capable of generating a radical cation. The ABTS assay stands out as one of the simplest and most reliable methods for analyzing the antioxidant activity of substances like albumin— a molecule that denatures in methanol solutions and under low pH conditions. In this study, the ABTS assay was performed following modified protocols originally described by Re et al. and Ilyasov et al (Re et al. 1999, Ilyasov et al. 2020). For assay preparation, a 7 mM ABTS solution and a 2.45 mM potassium persulfate solution were mixed at a 1:1 volume ratio, and the mixture was incubated overnight to facilitate reaction completion. Ascorbic acid was employed as the reference standard, with which a set of standard solutions with gradient concentrations was prepared. Following this, the ABTS working solution was combined with either the standard solution or the sample at a 200:1 volume ratio; the resulting mixture was then incubated at room temperature for 6 minutes under light-protected conditions. After incubation, absorbance is measured at either 414–417 nm or 730–734 nm. The absorbance of the standard solution detected at 732 nm is plotted to create a standard curve, and the absorbance of the samples is compared to this curve to calculate their antioxidant capacity.\u003c/p\u003e\n\u003cp\u003e2.8. Statistical analysis\u003c/p\u003e\n\u003cp\u003eA two-tailed unpaired Student’s \u003cem\u003et\u003c/em\u003e-test was applied to compare differences among groups and subgroups, with OriginPro 9.1 (OriginLab) and Prism 7 (GraphPad) serving as the data analysis tools. Meanwhile, SPSS Statistics V22.0 (IBM) was utilized to carry out correlation analysis, multivariate analysis of variance (MANOVA), and independent \u003cem\u003et\u003c/em\u003e-test. All measurement values following parametric distributions were expressed as mean ± standard deviation (mean ± SD). The criteria for statistical significance were set as follows: a \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05 indicated a statistically significant difference, and a \u003cem\u003ep\u003c/em\u003e-value between 0.05 and 0.1 was regarded as a borderline significant difference. All experimental procedures were repeated independently for at least three times to confirm the stability of the results.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1. Subjects recruited for this study\u003c/p\u003e\n\u003cp\u003eThis study enrolled 330 healthy volunteers from four distinct regions in Hubei Province; after excluding 15 participants due to insufficient sample volumes for six-marker analysis, the final cohort comprised 315 individuals (114 males, 201 females). The regional distribution of included participants was as follows: Xiaogan (n=90; 32 males, 58 females), Huangshi (n=47; 8 males, 39 females), Qianjiang (n=80; 28 males, 52 females), and Gongan (n=98; 46 males, 52 females) (Table 1). Participants underwent two follow-up visits during March (spring) and August (summer) of the study year. Complete demographic characteristics are presented in Table 1. In order to ensure that the sampling area has no significant effect on the experimental results, we conducted separate analysis of redox indicators for samples from different sampling areas to reveal the differences. At each visit, fasting serum samples were collected for comprehensive redox status assessment. All participants provided written informed consent prior to enrollment, in compliance with the institutional ethical requirements.\u003c/p\u003e\n\u003cp\u003e3.2. Heat Stress and Oxidative Dysregulation in Volunteers\u003c/p\u003e\n\u003cp\u003eProlonged exposure to high temperatures, both during the day and at night, induces cumulative physiological stress that disrupts redox homeostasis and increases the risk of heat-related health complications and mortality (Li et al. 2025). Epidemiological and clinical studies have established that heat stress disorders are associated with significant alterations in oxidative stress markers (Yang et al. 2024b). To systematically evaluate the association between environmental heat exposure and systemic redox imbalance, we conducted a comparative analysis of two distinct seasonal periods in Hubei Province, China: a thermoneutral reference period (March, mean temperature: 15.2 ± 3.1°C) and a peak heat stress period (August, mean temperature: 31.6 ± 2.8°C) (Table S1).\u003c/p\u003e\n\u003cp\u003eA heat wave is defined as a prolonged meteorological event characterized by sustained abnormally high temperatures that exceed regional adaptation capacities, resulting in significant impacts on human health, ecosystems, and infrastructure. The World Meteorological Organization recommends that the weather process with a daily maximum temperature higher than 32℃ and lasting more than 3 days is called a heat wave (C.H. HUANG Zhuo 2011). Our data showed that none of the four cities surveyed experienced a heat wave in the month of March in spring (Figures 1A-D). In contrast, in August, Gong 'an, Qianjiang and Xiaogan experienced 23 days of high-temperature heatwave conditions (Figures 1E-G), accounting for 74.2% of the total number of days in the month; Huangshi experienced 26 days of high-temperature heatwave conditions., accounting for 83.9% of the total number of days in the month (Figure 1F).\u003c/p\u003e\n\u003cp\u003eSubsequently, serum samples collected from the recruited volunteers in March and August were analyzed for six redox markers. Our findings revealed that serum nitrite levels and GSSG concentrations exhibited progressive declines of 21.01% and 5.63%, respectively, during the August heat period compared to measurements taken in March (Figures 2A, C). In contrast, we observed a 14.35% increase in MDA (Table 2; Figure 2D) concentrations and a 6.16% rise in T-AOC (Table 2; Figures 2E-F). We also collected serum samples from the same volunteer cohort in April, a period with temperatures similar to those observed in March (Figures S1A-D). Under conditions where the temperature has risen significantly but there is no high-temperature heatwave, the redox status of serum samples collected in April remained relatively stable (Figures S2A–F). These results indicated that nitrite, oxidation and reduction indices were affected to some extent by heat stress, with MDA and T-AOC\u003csub\u003e2\u003c/sub\u003e showing the most significant changes. Notably, the observed biochemical changes occurred in the absence of clinical manifestations, suggesting that even ostensibly healthy individuals experience significant redox perturbations under heat stress.\u003c/p\u003e\n\u003cp\u003e3.3. Heat stress induces sex-divergent redox responses\u003c/p\u003e\n\u003cp\u003eEmerging evidence from mammalian studies suggested that females exhibit superior ROS buffering capacity compared to males (Tiberi et al. 2023). To investigate sex-specific differences in oxidative stress\u0026nbsp;responses under heat challenge, we performed stratified analysis of redox markers in our cohort under seasonal temperature variations. Our findings revealed differential heat sensitivity in lipid peroxidation and antioxidant responses between sexes (Figure 3). Baseline levels of nitrate, nitrite, and GSSG showed no significant sex differences (all\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026gt; 0.05; Figures 3A-C). However, high-temperature exposure induced sex-divergent patterns in MDA and antioxidant capacity (Figure 3D). Notably, the T-AOC\u003csub\u003e2\u003c/sub\u003e was higher in males under thermoneutral conditions (March: male vs female, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e=0.012), but this difference disappeared under extreme heat (August: \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.38). The increase of T-AOC\u003csub\u003e2\u003c/sub\u003e in females after heat stress is the main factor responsible for the increase of T-AOC\u003csub\u003e2\u003c/sub\u003e levels (Figure 2F）, while there is no significant change in T-AOC\u003csub\u003e2\u003c/sub\u003e levels in males after heat stress. These results suggest that heat stress amplifies inherent sexual dimorphism in oxidative homeostasis. After excluding the influence of sex as much as possible, there was no doubt that MDA and nitrite levels showed significant changes under heat stress and were minimally affected by sex (Table S2).\u003c/p\u003e\n\u003cp\u003e3.4. Age-Independent Oxidative Stress Response to Heat Exposure\u003c/p\u003e\n\u003cp\u003eWhile cumulative evidence indicates that oxidative stress increases with age, and multiple redox parameters are established markers of aging (El Assar et al. 2024), the interaction between chronological aging and heat-induced oxidative damage in healthy populations remains poorly characterized. To fill this knowledge gap, we conducted stratified analyses of serum redox markers in four age quartiles (20-30 years, 30-40 years, 40-50 years, and 50-60 years) under different seasonal heat environments. The results showed that serum nitrate levels were positively correlated with age (r \u0026lt; 0.5), and this correlation persisted across time, sex, and temperature conditions – older individuals generally exhibited higher nitrate concentrations\u0026nbsp;(Liguori et al. 2018) (Figures 4B, H). Notably, MDA levels were not significantly correlated with age in March when temperatures were relatively mild (Figure 4D), but showed a significant positive correlation (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01) in August under high-temperature conditions (Figure 4J). In contrast, T-AOC\u003csub\u003e1\u003c/sub\u003e was weakly positively correlated with age (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) in March under mild temperatures, but this association disappeared in August (Figures 4E, K). Furthermore, serum levels of nitrite, GSSG, and T-AOC\u003csub\u003e2\u003c/sub\u003e in healthy individuals did not show significant associations with age in either March or August (Figures 4A, C, F). These results suggest that older individuals are more sensitive to heat-induced oxidative damage, with this vulnerability being most evident in the elevated MDA levels.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe increasing frequency and intensity of extreme heat events due to global climate change, urbanization, and the urban heat island effect have significantly elevated public health risks. Understanding the physiological responses to environmental heat stress is important for developing early warning systems and preventive strategies. In this study, we conducted a longitudinal cohort analysis involving 330 healthy individuals from four cities in Hubei Province, China, to investigate the impact of high-temperature exposure on serum redox homeostasis. Our findings demonstrated that environmental heat stress affected the body’s oxidative balance, as evidenced by reduced nitric oxide bioavailability, increased lipid peroxidation, and altered antioxidant defense capacity. Importantly, we identified MDA as a potentially sensitive and robust marker of heat-induced oxidative damage.\u003c/p\u003e\n\u003cp\u003eMDA is the core product of lipid peroxidation, and many studies have demonstrated the correlation between MDA and heat stress (Liu et al. 2021a). In plants, heat stress catalyzes the peroxidation of polyunsaturated fatty acids such as linolenic acid by activating enzymatic reactions such as lipoxygenase (LOX) to generate lipid hydroperoxides (LOOH), which are further decomposed to produce MDA. For example, in Arabidopsis and spinach, heat stress-induced MDA is mainly derived from the peroxidation of chloroplast membrane lipids (Kumar et al. 2023). In animal models, heat stress leads to mitochondrial dysfunction and free radical burst, accelerating MDA production through non-enzymatic pathways. For example, MDA content was significantly increased in the thigh muscle of heat-stressed broilers and was inversely correlated with decreased antioxidant enzyme activity (Ramiah et al. 2019). MDA production is also associated with heat stress-induced ferroptosis in mammals. In the mouse model of exhaustion heat stroke, heat stress up-regulates ACSL4 expression through the YAP/TEAD pathway, which promotes the insertion of arachidonic acid into phospholipids, exacerbates lipid peroxidation and MDA accumulation (He et al. 2022). This suggests that MDA accumulation is not only a consequence of oxidative damage but also a key link in heat stress-triggered programmed cell death, such as ferroptosis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur data revealed marked sexual dimorphism in the redox response to heat exposure. Male participants exhibited significantly higher MDA accumulation compared to females, suggesting greater susceptibility to lipid peroxidation under heat stress. Conversely, females demonstrated superior T-AOC\u003csub\u003e2\u003c/sub\u003e resilience, potentially reflecting protective effects conferred by estrogen-mediated antioxidant pathways. These findings show that sex differences may play a crucial role in the physiological response to environmental stressors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe also examined how age influences redox adaptations to heat stress. While baseline nitrate levels increased linearly with advancing age—a trend likely attributable to reduced endothelial nitric oxide synthase (eNOS) activity—this age-related increase was not further exacerbated by acute heat exposure. However, older individuals showed a more pronounced increase in MDA levels following heat stress, highlighting their heightened vulnerability to oxidative damage. Gongan, Qianjiang and Xiaogan experienced 23 days of high-temperature heatwave conditions. These findings suggest that while aging primarily affects basal redox regulation, it potentiates the harmful effects of environmental stressors such as heat.\u003c/p\u003e\n\u003cp\u003eAlthough extensive epidemiological investigations have documented heat-related changes in oxidative stress, there is an ongoing knowledge gap regarding the temporal dynamics of redox homeostasis in healthy populations, especially the possibility of MDA through longitudinal serum marker analysis before and after heat exposure. This study provides fundamental insights into the effects of high temperature environments on redox balance in healthy individuals. On this basis, we propose that MDA—identified as a key marker in this study and a core product of heat stress-induced lipid peroxidation—has the potential to serve as a biomarker for heat stress. In the future, it is necessary to further clarify its dynamic threshold, spatial-temporal distribution in different species, and its association with early stress signals to improve its utility as early warning markers. This will help to pay attention to population health in hot environment, provide important data for assessing the impact of high temperature on population health, and provide scientific basis for the development of public health policies.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Hong Zhou, Jiachen Dai, and Jun Wang; Methodology: Hong Zhou and Jiachen Dai; Validation: Yuemei Zhang, Wenwen Tang, Jiachen Dai and Yingying Ren; Formal Analysis: Xinming Zhang and Hong Zhou; Investigation: Hong Zhou, Jiachen Dai; Resources, George H. Lorimer, Hasan Bayram, Reza A Ghilad, Fanghua Mei and Junqiang Xu; Data Curation, Yuemei Zhang, Jiachen Dai and Hong Zhou; Writing – Original Draft Preparation, Hong Zhou and Jiachen Dai; Writing – Review \u0026amp; Editing, Hong Zhou, Jiachen Dai and Jun Wang; Funding Acquisition, Hong Zhou and Jun Wang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Collaborative Grant-in-Aid of the HBUT National \"111\" Center for Cellular Regulation and Molecular Pharmaceutics (XBTK-2024001) (Hong Zhou), and Hubei Provincial Department of\u0026nbsp;Education Innovation Group Grant\u0026nbsp;(4115/00257) (Jun Wang).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki, and approved by the Hubei Center for Disease Control and Prevention, Wuhan, People’s Republic of China (Approval Number: 2022-023-02, approved on 8 July 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all subjects involved in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cstrong\u003e2023.\u003c/strong\u003e Lancet Countdown: Heat-related Mortality.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBaksu, B., I. 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Free Radic Biol Med 175: 216-225.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eYang, X., L. Wang, K. Lu, X. Li, K. Song, and C. Zhang. 2024a.\u003c/strong\u003e High temperature induces oxidative stress in spotted seabass (Lateolabrax maculatus) and leads to inflammation and apoptosis. Fish Shellfish Immunol 154: 109913.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eYang, X., H. Wang, C. Shen, X. Dong, J. Li, and J. Liu. 2024b.\u003c/strong\u003e Effects of isorhamnetin on liver injury in heat stroke-affected rats under dry-heat environments via oxidative stress and inflammatory response. Sci Rep 14: 7476.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eYuan, Y., M. Tan, M. Zhou, M. J. Hassan, L. Lin, J. Lin, Y. Zhang, and Z. Li. 2024.\u003c/strong\u003e Drought priming-induced stress memory improves subsequent drought or heat tolerance via activation of γ-aminobutyric acid-regulated pathways in creeping bentgrass. Plant Biol (Stuttg).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Demographic characteristics of volunteers\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eXiaogan City\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=90\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHuangshi City\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=47\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eQianjiang City\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=80\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGongan City\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=98\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003esex (n, M/F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32/58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8/39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28/52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46/52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (mean ± SD)\u003c/p\u003e\n \u003cp\u003eAge (years old)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40±9\u003c/p\u003e\n \u003cp\u003e23 ~ 56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38±9\u003c/p\u003e\n \u003cp\u003e20 ~ 57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39±9\u003c/p\u003e\n \u003cp\u003e22 ~ 59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38±9\u003c/p\u003e\n \u003cp\u003e22 ~ 58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Summary of redox markers at sampling time\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eredox\u003c/strong\u003e\u003cstrong\u003emarker\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eConcentration\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(March)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eConcentration\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(August)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNitrate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.035 ~ 168.5 µM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.385 ~ 252 µM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNitrite\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1112 ~ 3.947 µM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1218 ~ 2.886 µM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGSSG\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02980 ~ 2.563 µM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0337 ~ 1.134 µM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMDA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0192 ~ 0.1896 µM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0054 ~ 0.2718 µM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT-AOC\u003csub\u003e1\u003c/sub\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0192 ~ 0.1896 mM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4478 ~ 1.651 mM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT-AOC\u003csub\u003e2\u0026nbsp;\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4462 ~ 2.146 mM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.775 ~ 4.298 mM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"heat stress, malondialdehyde, nitric oxide, antioxidant, redox marker","lastPublishedDoi":"10.21203/rs.3.rs-7350440/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7350440/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAgainst the backdrop of more frequent extreme heat events driven by climate change and urbanization, clarifying the physiological effects of environmental heat exposure\u0026mdash;particularly its impact on redox homeostasis in healthy populations\u0026mdash;and identifying biomarkers for early heat-induced stress detection have become urgent public health priorities. This longitudinal study enrolled 330 healthy volunteers from four cities in Hubei Province (China), with fasting serum samples collected in March (mild temperatures) and August (high temperatures) to measure and analyze six redox markers: nitrite (NO₂⁻), nitrate (NO₃⁻), oxidized glutathione (GSSG), malondialdehyde (MDA), and total antioxidant capacity (assessed via FRAP and ABTS assays). High-temperature exposure significantly disrupted redox balance, characterized by decreased nitric oxide bioavailability, elevated lipid peroxidation, and enhanced serum antioxidant activity. Among the measured markers, MDA exhibited the most sensitive and consistent response to heat stress, with little interference from age or sex\u0026mdash;supporting its potential as a reliable indicator for evaluating heat-induced oxidative damage. These insights may contribute to reducing health risks linked to extreme heat exposure.\u003c/p\u003e","manuscriptTitle":"Environmental Heat Stress Modulates Systemic Redox Homeostasis: A Longitudinal marker Analysis in Healthy Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 19:08:43","doi":"10.21203/rs.3.rs-7350440/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"99167f33-5a66-4a6a-aa6a-fab59514d9bc","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-06T16:37:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-11 19:08:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7350440","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7350440","identity":"rs-7350440","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}