Chronic psychological stress is increasingly recognized as a major contributor to physiological decline, influencing both physical and mental health across the lifespan. One of the proposed biological pathways through which stress exerts its deleterious effects is oxidative stress—a condition resulting from an imbalance between the production of reactive oxygen species (ROS) and the body’s ability to neutralize them with antioxidants. Oxidative damage, triggered by this imbalance, can affect key biological molecules such as DNA, RNA, lipids, and proteins, ultimately contributing to accelerated cellular aging and disease vulnerability [1]. The Free Radical Theory of Aging, originally proposed by Harman in 1956, posits that the accumulation of oxidative damage is a fundamental cause of aging and age-related diseases. This theory suggests that reducing oxidative stress may offer protective effects against cognitive and physical decline associated with aging. Although oxidative stress is not the sole determinant of aging, a substantial body of literature supports its role as a major mediator of age-related biological changes [2-3]. Myeloperoxidase (MPO) is a heme-containing enzyme abundantly expressed in neutrophils, macrophages, and monocytes. It plays a central role in the body’s innate immune defence by generating a diverse range of ROS, including hypochlorous acid (HOCl), hydroxyl radicals, and nitrogen dioxide, through the catalytic conversion of hydrogen peroxide [4]. While MPO is critical for host defence, it also contributes to tissue damage by amplifying oxidative stress. Elevated MPO levels have been associated with inflammatory and degenerative conditions, including atherosclerosis, cardiovascular disease, and neurodegenerative disorders. Due to its role in catalyzing ROS production and its measurable presence in serum, MPO has emerged as a clinically significant biomarker of oxidative stress [4]. The brain is particularly susceptible to oxidative stress due to its high oxygen consumption, abundance of unsaturated lipids, and relatively low antioxidant capacity. This makes it especially vulnerable to oxidative insults, which are implicated in numerous neuropsychiatric disorders, including anxiety, depression, and other mood disturbances [5]. The connection between psychological stress and oxidative stress is especially important among populations exposed to high cognitive demands and emotional challenges such as medical students. Medical education is widely acknowledged as a highly demanding and stressful experience. Studies report that approximately 63% of medical students experience significant stress, with females disproportionately affected (75.7% vs. 57% in males) [6]. This persistent psychological burden can impair cognitive performance, emotional resilience, and academic achievement. Alongside academic pressures, interpersonal conflicts and inadequate coping strategies further intensify the stress experienced by students. Additionally, sleep a vital physiological process essential for restoration and repair has a complex and bidirectional relationship with oxidative stress. Sleep disturbances can exacerbate oxidative damage, while oxidative stress can disrupt sleep regulation by interfering with redox-sensitive signaling mechanisms [7]. Thus, sleep quality is not only an outcome of stress but also a potential contributor to oxidative imbalance. The objective of this study is to investigate the relationship between psychological stress, sleep quality, and serum myeloperoxidase levels in medical undergraduate students, comparing stressed and non-stressed individuals.

This case-control study, conducted over six months at the Department of Physiology, HIMS, Safedabad, Uttar Pradesh, India, aimed to investigate the relationship between oxidative stress, as measured by serum MPO levels, and psychological stress in medical students. The study population comprised MBBS students who were recruited through institutional communication channels. Eligible participants were invited to join the study after providing written informed consent.

Inclusion and Exclusion Criteria

• Inclusion Criteria:

MBBS students enrolled in the academic program across all years. Only apparently healthy student was included.

• Exclusion Criteria:

Students with obesity (BMI >30), waist circumference ≥35 inches in females and ≥40 inches in males, known cases of polycystic ovarian disease (PCOD), smokers, students on medication for psychiatric disorders, those with acute or chronic infections, any diagnosed psychiatric illness, or chronic systemic illnesses were excluded.

Sampling and Group Allocation

Participants were assessed using two validated psychometric tools:

1. Pittsburgh Sleep Quality Index (PSQI) – to assess sleep quality (PSQI = Pittsburgh Sleep Quality Index. A global score of ≤ 5 indicates good sleep, while a score > 5 indicates poor sleep quality, as per standardized criteria [8]. Poor sleepers showed elevated MPO levels, reflecting increased oxidative stress.
2. Student Stress Inventory (SSI) – to measure the level of stress among university students. SSI contained of 40 negative items to measure 4 subscales (10 items for each subscale) which are sub scale 1: Physical (10 items), sub scale 2: Interpersonal relationship (10 items), sub scale 3: Academic (10 items) and subscale 4: Environmental factor (10 items). As for scoring, SSI was designed with ordinal scale of the ‘Never’, ‘Somewhat frequent’, ‘Frequent’ and ‘Always’.  The value mark given for each choice are 1 for ‘Never’, 2 for ‘Somewhat Frequent’, 3 for ‘Frequent’ and 4 for ‘Always’ [9].

These tools were administered using a Google Forms digital questionnaire. Based on scores:

• Group 1 (Cases): 50 students who reported psychological stress
• Group 2 (Controls): 50 students who reported no psychological stress

Anthropometric Measurements

Participants were invited to the Department of Physiology for history taking and anthropometric evaluation, which included measurements of height, weight, BMI, and waist circumference using standardized instruments.

Biochemical Investigations

Blood samples were drawn from all participants for serum MPO analysis. As MPO levels are not significantly affected by recent meals, fasting was not required before sample collection.

Laboratory Procedure: Estimation of MPO Levels: Blood Sample Collection and Serum Preparation

Venous blood samples (5 mL) were collected from participants into plain vacutainer tubes without anticoagulant. After clot formation at room temperature for 30 minutes, samples were centrifuged at 3000 rpm for 10 minutes. Serum was separated, transferred to pre-labeled Eppendorf tubes, and stored at -20°C.

Estimation of Serum Myeloperoxidase (MPO) Levels: Serum MPO concentrations were determined using a quantitative sandwich Enzyme-Linked Immunosorbent Assay (ELISA) with a commercially available Human MPO ELISA kit, following the manufacturer's guidelines. The assay procedure included:

Sample and Reagent Preparation: Standards and samples were prepared according to the kit instructions.

ELISA Protocol: Standards and samples were added to designated wells in duplicate, followed by incubation with biotinylated detection antibody, enzyme conjugate, and TMB substrate. Optical density was measured at 450 nm using a microplate ELISA reader.

Standard Curve Generation: Sample concentrations were calculated from the standard curve and expressed in ng/mL.

Quality Control and Assay Validation: Samples were analyzed in duplicate to ensure reproducibility. The ELISA kit's sensitivity range was 0.1-10 ng/mL, with intra- and inter-assay variation below 10%. Laboratory procedures followed institutional safety guidelines and standard operating protocols [10].

Statistical Analysis

Data were entered in Microsoft Excel and analyzed using SPSS version 16.0 SPSS Inc. Chicago, U.S. Continuous variables were presented as mean ± SD. Group comparisons were made using independent t-tests parametric data. Chi-square test was used to test the association between categorical variable. Using scatter diagram for trend between serum MPO level with BMI, Age and waist circumference. A p-value <0.05 was considered statistically significant.

The baseline characteristics of the study participants showed some differences between the stressed and non-stressed groups. While age and gender distribution were similar between the two groups, significant differences were observed in body mass index (BMI) and waist circumference. The stressed group had a slightly higher mean BMI (p = 0.020) and waist circumference (p = 0.011) compared to the non-stressed group. However, the distribution of students across different years of study was comparable between the two groups. These findings suggest that stress may be associated with certain physical health parameters. (table 1)

Table 1: Baseline Characteristics of Participants

*Independent t test; **Chi-square test

The scatter diagrams (figures 1-3) illustrate the relationships between serum MPO levels and various physical parameters, including BMI, age, and waist circumference. The R-square values indicate that approximately 4.5% of the variation in serum MPO levels can be attributed to BMI, suggesting a weak positive relationship. In contrast, age has a negligible relationship with MPO levels, accounting for only 0.2% of the variation. Waist circumference has a moderate positive relationship with MPO levels, explaining around 7% of the variation.

Mean and standard deviation values (figures 4-7) for various parameters showed some similarities and slight differences between males and females. MPO levels are marginally higher in males (7.0 ± 1.1) compared to females (6.9 ± 1.2). Waist circumference values are also slightly higher in males (80.2 ± 3.8) compared to females (79.6 ± 3.4). Sleep quality, as measured by the PSQI score, is comparable between males (5.4 ± 1.2) and females (5.5 ± 1.4). Stress levels, as measured by the SSI score, are slightly higher in males (68.3 ± 10.4) compared to females (66.8 ± 11.6), but the difference is not substantial.

The comparison of mean serum MPO levels between stressed and non-stressed groups in table 2 showed that the stressed group had a mean MPO level of 7.9 ng/mL (SD = 0.8, SEM = 0.11), whereas the non-stressed group had a mean MPO level of 6.0 ng/mL (SD = 0.6, SEM = 0.08). The independent t-test showed a highly significant difference between the two groups (p < 0.001), indicating that stressed individuals have significantly higher serum MPO levels compared to non-stressed individuals. This finding suggests a potential link between stress and oxidative stress, as measured by MPO levels.

Table 2: Comparison of Mean Serum MPO Levels Between Stressed and Non-Stressed Groups

*Independent t test

Table 3 illustrates that the association between sleep quality and stress status was examined using the PSQI scores. Among stressed students, 36 (69.2%) had poor sleep quality (PSQI > 5), whereas among non-stressed students, 34 (70.8%) had good sleep quality (PSQI ≤ 5). The chi-square test revealed a highly significant association between sleep quality and stress status (p < 0.001), indicating that stressed students are more likely to experience poor sleep quality, while non-stressed students tend to have better sleep quality.

Table 3: Association Between Sleep Quality and Serum MPO Levels

*Chi-square test

In table 4, Pearson correlation analysis showed significant relationships between serum MPO levels, stress levels (SSI score), and sleep quality (PSQI score) among stressed and non-stressed individuals. In the stressed group, there was a moderate positive correlation between PSQI score and serum MPO level (r = 0.45, p = 0.001), indicating that poorer sleep quality was associated with higher MPO levels. In contrast, no significant correlation was found in the non-stressed group (r = 0.14, p = 0.321). Notably, the SSI score showed a strong positive correlation with serum MPO level in both stressed (r = 0.99, p < 0.001) and non-stressed groups (r = 0.98, p < 0.001), suggesting a robust relationship between stress levels and MPO levels regardless of stress status.

Table 4: Pearson Correlation Between SSI Score PSQI Score and Serum MPO (ng/mL) level among both groups (Stressed and Non- Stressed)

Medical undergraduate students often face significant psychological stress due to academic demands, clinical responsibilities, and the high-stakes environment of medical education. This stress can lead to increased oxidative stress, as evidenced by elevated levels of oxidative stress markers such as MDA and nitric oxide (NOx). While MPO levels in this specific population have not been directly studied, the established association between psychological stress and oxidative stress suggests that MPO could serve as a useful biomarker in this context [11]. Present study observed significantly elevated serum MPO levels in psychologically stressed medical students compared to their non-stressed counterparts, suggesting increased oxidative stress associated with psychological stress. While direct studies on MPO levels in medical students are limited, research indicates that psychological stress can lead to increased oxidative stress markers. For instance, a study by Irwin et al. (2008) demonstrated that sleep loss, a common consequence of stress, activates nuclear factor (NF)-κB, a transcription factor that plays a critical role in the inflammatory signaling cascade, thereby linking stress to oxidative stress pathways [12]. Additionally, Kiecolt-Glaser et al. (2003) found that chronic stress in caregivers was associated with increased oxidative stress, further supporting the connection between psychological stress and oxidative stress [13].  The identification of MPO as a marker of oxidative stress in psychologically stressed medical undergraduate students has important implications for both diagnosis and treatment. MPO levels could serve as a non-invasive biomarker for monitoring oxidative stress in this population, allowing for early identification of individuals at risk of developing stress-related disorders. Additionally, interventions aimed at reducing oxidative stress, such as antioxidant supplementation or stress management techniques, could potentially mitigate the negative effects of psychological stress on mental and physical health [14,15]. Furthermore, present study found a moderate positive correlation between PSQI scores and MPO levels in the stressed group, indicating that poorer sleep quality is associated with increased oxidative stress. Additionally, SSI scores showed a very strong positive correlation with MPO levels in both stressed and non-stressed groups. Although few studies directly measure MPO in student populations, findings from Irwin et al. (2009) support the underlying physiological mechanism; their study showed that sleep loss activates cellular markers of inflammation, including interleukins and oxidative markers, suggesting that sleep disruption plays a key role in stress-related immune activation [12]. Vgontzas et al. (2003) also found that chronic insomnia leads to hyperactivation of the hypothalamic–pituitary–adrenal (HPA) axis, which is a key stress pathway known to influence oxidative stress responses [16]. These findings strengthen the view that oxidative stress, as indexed by MPO levels, is closely intertwined with both psychological stress and sleep disturbances, especially among students facing academic pressure. In present study, a significant association was found between poor sleep quality (PSQI > 5) and higher stress levels among medical students. This finding aligns with several studies in the literature.  Almojali et al. (2017) reported a high prevalence of poor sleep quality (76%) and stress (53%) among medical students, with a statistically significant association between the two [17]. Similarly, Safhi et al. (2020) found that 65% of medical students experienced stress, and 76.4% had poor sleep quality, with a strong association between stress and poor sleep quality. These studies underscore the pervasive impact of psychological stress on sleep quality among medical students [18].​  Present study observed significantly higher BMI and waist circumference in stressed students, suggesting a physiological impact of chronic stress on body composition. This observation is consistent with research by Epel et al. (2000), who found that women with greater stress levels exhibited increased abdominal fat accumulation, mediated by higher cortisol responses [19].  Similarly, Block et al. (2009) reported that psychosocial stress was associated with increased BMI and waist circumference among U.S. adults [20].  Within student populations, Almojali et al. (2017) noted that stress-induced behavioral changes, including irregular sleep and poor eating habits, could potentially contribute to increased adiposity [17]. These findings support that stress in academic environments may lead to adverse physical health outcomes, even in young, otherwise healthy populations like medical students. Additionally, present study found serum MPO levels and stress scores were slightly higher in male students compared to females, though the differences were not statistically significant. This finding aligns with research by Kudielka and Kirschbaum (2005), who reported that men generally exhibit greater physiological stress responses, particularly in HPA axis activity, compared to women [21]. However, in contrast, some student-focused studies, such as Safhi et al. (2020), reported higher stress prevalence among female students, possibly reflecting gender-based differences in emotional coping or societal expectations [18]. These mixed findings suggest that while physiological responses (e.g., MPO levels) may differ slightly by gender, psychological perceptions and coping mechanisms also play significant roles, necessitating more analysis in future student stress research.

This study provides important relationship between psychological stress, sleep quality, and oxidative stress among medical undergraduate students. Serum MPO levels were significantly elevated in psychologically stressed students. Additionally, the significant associations observed between MPO levels, poor sleep quality, and stress severity emphasize the physiological burden placed on students experiencing academic and interpersonal stress. Anthropometric differences, such as higher BMI and waist circumference in the stressed group, suggest that chronic psychological stress and potentially increasing long-term risk for metabolic disorders. The positive correlation between stress scores and MPO levels across both stressed and non-stressed groups highlights MPO's potential as a reliable biomarker for oxidative stress in young adults. The application of serum MPO as a non-invasive biomarker could pave the way for early detection of stress-induced physiological changes.

1. Aschbacher K, O'Donovan A, Wolkowitz OM, Dhabhar FS, Su Y, Epel E. Good stress, bad stress and oxidative stress: insights from anticipatory cortisol reactivity. Psychoneuroendocrinology. 2013 Sep;38(9):1698-708.
2. Harman D. Aging: a theory based on free radical and radiation chemistry. J Gerontol. 1956; 11:298– 300.
3. Muller FL, Lustgarten MS, Jang Y, Richardson A, Van Remmen H. Trends in oxidative aging theories. Free Radic Biol Med. 2007; 43:477–503.
4. Ho E, Karimi Galougahi K, Liu CC, Bhindi R, Figtree GA. Biological markers of oxidative stress: Applications to cardiovascular research and practice. Redox Biol. 2013 Oct 8;1(1):483-91.
5. Salim S. Oxidative stress and psychological disorders. Curr Neuropharmacol.2014Mar;12(2):140-7.
6. Abdulghani HM, AlKanhal AA, Mahmoud ES, Ponnamperuma GG, Alfaris EA. Stress and its effects on medical students: a cross-sectional study at a college of medicine in Saudi Arabia. J Health Popul Nutr. 2011 Oct;29(5):516-22.
7. Davinelli S, Medoro A, Savino R, Scapagnini G. Sleep and Oxidative Stress: Current Perspectives on the Role of NRF2. Cell Mol Neurobiol. 2024 Jun 25;44(1):52.
8. Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989 May;28(2):193-213.
9. Arip MA, Kamaruzaman DN, Roslan A, Ahmad A. Manual Student Stress Inventory (SSI): Development, Validity and Reability of Student Stress Inventory (SSI). Faculty of Education and Human Development Sultan Idris Education University. Malaysia. 2016.
10. Pulli B, Ali M, Forghani R, Schob S, Hsieh KL, Wojtkiewicz G, Linnoila JJ, Chen JW. Measuring myeloperoxidase activity in biological samples. PLoS One. 2013 Jul 5;8(7):e67976.
11. Brinholi FF, Vasupanrajit A, Semeão LO, Michelin AP, Matsumoto AK, Almulla AF, Tunvirachaisakul C, Barbosa DS, Maes M. Increased malondialdehyde and nitric oxide formation, lowered total radical trapping capacity coupled with psychological stressors are strongly associated with the phenome of first-episode mild depression in undergraduate students. Neuroscience. 2024 Aug 30;554:52-62.
12. Irwin MR, Wang M, Ribeiro D, Cho HJ, Olmstead R, Breen EC, Martinez-Maza O, Cole S. Sleep loss activates cellular inflammatory signaling. Biol Psychiatry. 2008 Sep 15;64(6):538-40.
13. K. Kiecolt-Glaser,K.J. Preacher,R.C. MacCallum,C. Atkinson,W.B. Malarkey,& R. Glaser, Chronic stress and age-related increases in the proinflammatory cytokine IL-6, Proc. Natl. Acad. Sci. U.S.A. 2003; 100 (15): 9090-9095.
14. Hanafi ISC, Saravanan C, Basria, Mydin RSMN.  Oxidative Stress: Insights into Nutrition, Psychological Stress, Environmental Exposure, and Antioxidants Roles, Journal of Angiotherapy. 2024; 8(3): 1-6
15. Emekdar G, Taş H. Şehitoğlu, H. Investigation of the Relationship between Inflammation and Oxidative Stress Markers and Treatment Response in First-Attack Major Depression Patients: A Follow-Up Study. Turkish Journal of Psychiatry. 2023;34(2): 89–99.
16. Vgontzas AN, Tsigos C, Bixler EO, Stratakis CA, Zachman K, Kales A, Vela-Bueno A, Chrousos GP. Chronic insomnia and activity of the stress system: a preliminary study. J Psychosom Res. 1998 Jul;45(1):21-31.
17. Almojali AI, Almalki SA, Alothman AS, Masuadi EM, Alaqeel MK. The prevalence and association of stress with sleep quality among medical students. J Epidemiol Glob Health. 2017 Sep;7(3):169-174.
18. Safhi MA, Alafif RA, Alamoudi NM, Alamoudi MM, Alghamdi WA, Albishri SF, Rizk H. The association of stress with sleep quality among medical students at King Abdulaziz University. J Family Med Prim Care. 2020 Mar 26;9(3):1662-1667.
19. Epel ES, McEwen B, Seeman T, Matthews K, Castellazzo G, Brownell KD, Bell J, Ickovics JR. Stress and body shape: stress-induced cortisol secretion is consistently greater among women with central fat. Psychosom Med. 2000 Sep-Oct;62(5):623-32.
20. Block JP, He Y, Zaslavsky AM, Ding L, Ayanian JZ. Psychosocial stress and change in weight among US adults. Am J Epidemiol. 2009 Jul 15;170(2):181-92.
21. Kudielka BM, Kirschbaum C. Sex differences in HPA axis responses to stress: a review. Biol Psychol. 2005 Apr;69(1):113-32.