Malondialdehyde–Superoxide Dismutase Redox Imbalance as an Early Biological Risk Indicator in Young Adult Smokers

Abstract

Purpose

Young adult smokers represent a population in which smoking-related health risks may accumulate silently before the onset of clinically apparent disease. Although smoking-related changes in malondialdehyde (MDA) and superoxide dismutase (SOD) have been widely reported, less is known about their combined oxidant–antioxidant pattern in apparently healthy young adults during the early smoking trajectory. This study aimed to compare serum MDA and SOD levels between young adult smokers and non-smokers and to evaluate the MDA–SOD interplay as a marker of early redox imbalance.

Patients and Methods

A comparative cross-sectional study was conducted involving 190 participants aged 18–25 years, comprising 95 smokers and 95 non-smokers recruited from university communities. Smoking exposure was assessed using structured interviews and quantified by the Heaviness of Smoking Index and serum cotinine level. Serum MDA, SOD, and cotinine levels were measured using enzyme-linked immunosorbent assay methods. Group comparisons were performed using appropriate parametric or non-parametric tests, and associations between oxidative stress biomarkers were analysed using Spearman and Pearson correlation.

Results

Smokers exhibited significantly higher serum MDA concentrations than non-smokers [median 3.28 nmol/mL (range, 1.75–5.89) vs 2.27 nmol/mL (range, 0.99–5.83); p < 0.001] and significantly lower SOD activity (132.4 ± 26.7 vs 158.3 ± 29.1 U/L; p < 0.001). Across all participants, serum cotinine was positively associated with MDA (Spear man’s ρ = 0.576, p < 0.001) and inversely associated with SOD (Pearson’s r = −0.518, p < 0.001). MDA was also inversely associated with SOD (Spearman’s ρ = −0.318, p < 0.001). These findings indicate a measurable oxidant–antioxidant imbalance associated with biochemical tobacco exposure in young adults.

Conclusion

Young adult smokers exhibit early oxidant–antioxidant imbalance indicative of subclinical biological vulnerability associated with smoking exposure. These findings highlight the relevance of the MDA–SOD redox interplay as a biologically coherent marker pair for assessing early smoking-related redox disturbance in young adults.

Introduction

Cigarette smoking is a well-established exogenous source of reactive oxygen species (ROS) and reactive nitrogen species (RNS), generating a substantial oxidative burden that disrupts cellular and systemic redox homeostasis.¹ Each puff of cigarette smoke introduces a complex mixture of free radicals and pro-oxidant compounds capable of overwhelming endogenous antioxidant defences, particularly under habitual exposure.² When oxidant generation exceeds antioxidant capacity, oxidative stress ensues, triggering early molecular disturbances implicated in endothelial dysfunction, mitochondrial impairment, redox-sensitive signalling dysregulation, and increased tissue vulnerability.³ Importantly, these biochemical alterations often precede overt clinical manifestations, positioning oxidative stress as an early biological signal of future health risk rather than an immediate disease outcome.

One of the most prominent consequences of excessive ROS exposure is lipid peroxidation, a chain reaction that damages polyunsaturated fatty acids within cellular membranes. Malondialdehyde (MDA), a stable terminal product of lipid peroxidation, is widely recognised as a reliable biomarker of oxidative lipid injury and systemic oxidative stress. Elevated MDA concentrations reflect enhanced membrane damage and are closely linked to inflammatory activation and altered cellular function.^4,5 In contrast, superoxide dismutase (SOD) represents a primary enzymatic antioxidant defence, catalysing the dismutation of superoxide radicals into hydrogen peroxide and molecular oxygen.⁶ Alterations in SOD activity therefore serve as sensitive indicators of compromised antioxidant capacity.⁷ Rather than interpreting MDA or SOD in isolation, their combined evaluation provides a biologically coherent representation of redox imbalance: MDA reflects downstream oxidative lipid injury, whereas SOD reflects the enzymatic antioxidant response to superoxide burden. The reciprocal behaviour of these two biomarkers, conceptualised as the MDA–SOD redox interplay, offers a mechanistically informative framework for evaluating oxidant–antioxidant balance and early biological vulnerability in vivo.

Although smoking-related oxidative stress has been extensively documented, important knowledge gaps remain regarding how these markers behave together during the early stages of smoking exposure, particularly among apparently healthy young adults. Existing evidence has predominantly focused on older individuals or populations with established cardiometabolic or pulmonary comorbidities. In contrast, young adult smokers remain comparatively underrepresented in redox biology and health risk research, despite young adulthood representing a critical window during which chronic oxidative exposure may initiate subtle biochemical perturbations that predispose individuals to long-term disease trajectories.⁸ Emerging evidence indicates that markers of oxidative stress and impaired antioxidant capacity can already be detected in young smokers at a subclinical stage, even in the absence of symptoms or diagnosed disease, suggesting that biological risk accumulation begins early in the smoking trajectory.^9,10 Therefore, the novelty of the present study lies not in assessing MDA or SOD as standalone biomarkers, but in examining their oxidant–antioxidant relationship in young adult smokers before clinically apparent disease is present.

However, findings regarding antioxidant responses in early-stage smokers remain inconsistent. While some studies report transient upregulation of antioxidant enzymes as an adaptive response during initial exposure, others demonstrate a significant reduction in SOD activity, indicating early exhaustion or inactivation of enzymatic defences.^11,12 These discrepancies may reflect heterogeneity in smoking intensity, duration, population characteristics, dietary antioxidant intake, physical activity, environmental exposures, and analytical approaches, underscoring the limited clarity surrounding early redox dynamics in young smokers and highlighting the need for focused investigation in this demographic. A focused assessment of the MDA–SOD interplay may therefore provide useful biological insight into early smoking-related oxidative imbalance, while remaining appropriate to the scope of a cross-sectional biomarker study. Accordingly, this study aimed to evaluate oxidant–antioxidant imbalance by comparing serum malondialdehyde (MDA) and superoxide dismutase (SOD) levels between young adult smokers and non-smokers, with the objective of identifying early biological risk indicators relevant to preventive healthcare planning and tobacco-related risk management.

Materials and Methods

Study Design and Participant Recruitment

The comparative cross-sectional study was designed to evaluate oxidant–antioxidant status among young adult smokers and non-smokers. Participants were recruited from university communities and surrounding areas through purposive sampling. Eligible individuals were between 18 and 25 years of age, were in self-reported good health, and did not present with any chronic medical conditions. Smokers were defined as individuals who had consumed at least one cigarette per day for a minimum duration of six consecutive months. Smoking status was verified through a structured interview that recorded the number of cigarettes smoked per day, duration of smoking, and type of tobacco product used.

Non-smokers were recruited through a parallel screening process. Individuals were classified as non-smokers only if they had never engaged in active smoking, had no meaningful history of second-hand smoke exposure—operationalised as less than one hour of exposure per day for fewer than three days per week—and had not used nicotine delivery systems such as electronic cigarettes, vaping devices, or heated tobacco products. Individuals with occasional past smoking or experimental use within the preceding year were excluded. Additional screening ensured that non-smokers were not routinely exposed to household or occupational smoke to prevent confounding effects associated with passive smoking. Smoking status was determined based on participants’ self-report and verified using biomarkers such as serum cotinine levels.

Participants were included in the study if they were within the specified age range, had a BMI between 18.5 and 29.9 kg/m², and provided written informed consent. Individuals were excluded if they had any chronic illness, were taking antioxidant supplements or anti-inflammatory medication, reported excessive alcohol consumption, or had experienced an acute infection within the previous two weeks. Haemolysed samples were excluded from the analysis.

Sample Size Determination

The sample size was determined using a two-sided independent means approach, assuming a small anticipated effect size (Cohen’s d = 0.3), which is appropriate for oxidative stress biomarkers known to exhibit substantial biological variability. With a significance level of 0.05 and a statistical power of 80%, the minimum required number of participants was 88 per group (176 total).

To account for potential attrition, including participant withdrawal and exclusion of compromised samples, a 10% margin was added. Accordingly, the final target sample size was 95 participants per group (190 total) to ensure adequate statistical precision and robustness. This sample size was considered sufficient to detect early biological differences relevant to risk stratification at a population level.

Venous blood samples (5 mL) were collected from the antecubital vein between 08:00 and 10:00 h following an overnight fast of 8–10 hours. Serum samples were allowed to clot for 20 minutes at room temperature before centrifugation at 2,000–3,000 rpm for 20 minutes to obtain clear supernatants. Because both MDA and SOD were measured in serum obtained from blood collected without anticoagulants, anticoagulant type did not affect the biomarker measurements. All samples were stored at −80°C until biochemical analysis and underwent only a single freeze–thaw cycle. In accordance with the manufacturer’s instructions, samples were analysed without dilution.

Reagents and Assay Kits

Serum concentrations of SOD and MDA were measured using the Human SOD ELISA Kit (BT-Laboratory, Cat. No. E0918Hu) and the Human MDA ELISA Kit (BT-Laboratory, Cat. No. E1371Hu), respectively. Both assays are designated Research Use Only (RUO). The analytical ranges of the kits were 3–900 U/L for SOD and 0.2–70 nmol/mL for MDA, with respective sensitivities of 1.52 U/L and 0.14 nmol/mL. All procedures were performed strictly according to the manufacturer’s protocols.

ELISA Procedures for MDA and SOD

All ELISA analyses were performed at room temperature using a microplate reader set to 450 nm. For each analyte, 40 μL of serum was pipetted into wells pre-coated with the specific capture antibody, followed by the addition of 10 μL of the appropriate biotinylated detection antibody and 50 μL of streptavidin–HRP conjugate. Plates were incubated for 60 minutes at 37°C and subsequently washed five times with wash buffer to remove unbound components. The enzymatic colour reaction was initiated by adding substrate solutions A and B and incubating the plates for 10 minutes at 37°C in the dark. The reaction was stopped using the provided stop solution, and absorbance readings were obtained within 10 minutes of reaction termination. All samples were analysed in duplicate, and wells showing pipetting or optical anomalies were reassessed.

Standard Curve and Concentration Calculation

Standard curves for SOD and MDA were generated using six serial two-fold dilutions supplied with each kit. Mean absorbance values for the standard concentrations were plotted against their nominal values using a four-parameter logistic (4-PL) regression model. Serum concentrations of the analytes were interpolated automatically from the fitted curve. Samples with absorbance values outside the defined range of the standard curve were re-analysed to ensure measurement accuracy.

Assay Validation and Quality Control

Assay performance was validated by evaluating intra-assay and inter-assay coefficients of variation, with acceptable precision defined as CV values below 10%, in line with the kit specifications. Each assay plate included blanks, internal controls, and manufacturer-supplied standards to ensure inter-plate consistency. Plates exhibiting excessive background absorbance, unstable colour development, or deviations in the standard curve were re-evaluated, and the assay was repeated where necessary.

Assessment of Smoking Exposure

Smoking exposure was evaluated using a structured questionnaire that captured information on participants’ average daily cigarette consumption, duration of smoking, and the type of cigarette used. Nicotine dependence was quantified using the Heaviness of Smoking Index (HSI), a validated instrument based on daily cigarette consumption and time to first cigarette after waking.¹³ Each parameter was scored according to standard HSI criteria, and the total score (ranging from 0 to 6) was used to categorise participants into low (0–1), moderate (2–4), or high (5–6) dependence levels. The use of the Heaviness of Smoking Index allowed stratification of smoking exposure relevant to early health risk assessment.

Serum cotinine concentration was measured as an objective biomarker of tobacco exposure using a commercially available enzyme-linked immunosorbent assay (ELISA) kit (Cat. No. 285286; Abcam, China) according to the manufacturer’s instructions. All reagents and serum samples were brought to room temperature and gently mixed for 30 minutes before the assay. Briefly, 10 µL of standards, controls, and serum samples were added in duplicate to the designated microplate wells, followed by 100 µL of enzyme conjugate. The plate was shaken for 10–30 seconds to ensure thorough mixing and incubated at room temperature for 60 minutes, preferably in the dark. The wells were then washed six times with 300 µL of 1× wash buffer, and residual moisture was removed by inverting and tapping the plate on dry absorbent paper. Subsequently, 100 µL of substrate reagent was added to each well, followed by incubation at room temperature for 30 minutes in the dark. The reaction was terminated by adding 100 µL of stop solution, and the plate was gently shaken to mix. Absorbance was measured at 450 nm using a microplate reader within 15 minutes. Serum cotinine concentrations were calculated from the standard calibration curve and expressed as ng/mL.

Statistical Analysis

Data analysis was conducted using SPSS (v.25). Data normality was assessed using the Shapiro–Wilk test, which is recommended for small-to-moderate sample sizes. Normally distributed continuous data were presented as mean ± standard deviation, whereas non-normally distributed data were presented as median and interquartile range. Group comparisons were performed using independent t-tests for normally distributed variables and the Mann–Whitney U-test for non-normally distributed variables, including MDA. Chi-square tests were used for categorical variables. Correlation analyses between the Smoking Index and biomarker levels (MDA and SOD) were performed using Spearman correlation coefficients. The association between MDA, SOD, and cotinine among smokers was also assessed using Spearman and Pearson correlation. A p-value of less than 0.05 was considered statistically significant.

Ethical Approval

The study protocol received approval from the Health Research Ethics Committee Faculty of Health Sciences Universitas Brawijaya (No. 12757/UN10.F17.10.4/TU/2024), and all procedures were conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants prior to enrolment.

Results

A total of 190 participants were included in the final analysis, consisting of 95 smokers and 95 non-smokers (Table 1). The two groups were comparable in terms of age, sex distribution, body mass index (BMI), height, and weight, with no statistically significant differences observed (all p-value > 0.05).

Table 1.

Baseline Characteristics of Study Participants

Variable	Smokers (n = 95)	Non-Smokers (n = 95)	p-value
Age (years)	21.7 ± 2.2	21.3 ± 2.3	0.142a
Sex (n; %)
Male	66 (69.5%)	61 (64.2%)	0.84b
Female	29 (30.5%)	34 (35.8%)
Body Mass Index (kg/m2)	24.3 ± 3.4	24.3 ± 3.5	0.972a
Height (cm)	171.9 ± 7.4	171.1 ± 7.7	0.439a
Weight (kg)	71.8 ± 9.9	71.3 ± 11.7	0.769a
Duration of smoking (years)	3.0 ± 1.2	—	—
Cigarettes per day	9.6 ± 3.6	—	—
Heaviness of Smoking Index (HSI)	2.0 ± 1.3	—	—
Low dependence (HSI = 0–1)	37 (38.9%)	—	—
Moderate dependence (HSI = 2–4)	58 (61.1%)	—	—
High dependence (HSI = 5–6)	0 (0%)	—	—
Cotinine level (ng/mL)	290.5 ± 62.46	2.73 ± 1.87	<0.001

Notes: Values are expressed as mean ± standard deviation unless otherwise indicated. ^aIndependent t-test; ^bChi-square test. No statistically significant differences were observed between smokers and non-smokers in age, sex distribution, BMI, height, or weight (p-value > 0.05).

Among smokers, the mean duration of smoking was 3.0 ± 1.2 years, with an average consumption of 9.6 ± 3.6 cigarettes per day. The mean Heaviness of Smoking Index (HSI) score was 2.0 ± 1.3, corresponding predominantly to moderate nicotine dependence. Specifically, 37 participants (38.9%) were classified as having low dependence (HSI 0–1), while 58 participants (61.1%) demonstrated moderate dependence (HSI 2–4). No participants were classified as having high nicotine dependence (HSI 5–6).

Oxidant–Antioxidant Biomarker Profile

Comparisons of oxidative stress biomarkers between smokers and non-smokers are presented below. Assessment of data distribution indicated that serum MDA values were non-normally distributed, whereas SOD values followed a normal distribution. Serum malondialdehyde (MDA) concentrations were significantly higher in smokers 3.28 nmol/mL (range: 1.75–5.89), compared with 2.27 nmol/mL (range: 0.99–5.83) in non-smokers (p < 0.001). In contrast, superoxide dismutase (SOD) activity was significantly lower among smokers (132.4 ± 26.7 U/L) than among non-smokers (158.3 ± 29.1 U/L; p < 0.001). These findings indicate clear group differences in oxidative stress and antioxidant enzyme activity between young adult smokers and non-smokers (Figure 1).

Figure 1.

Boxplot comparison of serum MDA levels and SOD activity between smokers and non-smokers. (A) The MDA concentration was significantly elevated among smokers, indicating enhanced lipid peroxidation and oxidative stress (median with interquartile ranges). (B) Smokers exhibited significantly reduced SOD enzymatic activity (mean ± SD), reflecting impaired antioxidant defense capacity.

Association Between MDA, SOD, and Cotinine

Correlation analyses were conducted to examine the relationships among serum cotinine and oxidative stress biomarkers (Table 2). Across all participants (n = 190), serum cotinine was moderately and positively associated with MDA concentrations (Spearman’s ρ = 0.576, p < 0.001), indicating that higher cotinine levels tended to accompany higher lipid peroxidation. In contrast, cotinine was moderately and inversely associated with SOD activity (Pearson’s r = −0.518, p < 0.001), indicating that higher cotinine levels were associated with lower antioxidant enzyme activity. A statistically significant modest inverse association was also observed between MDA concentrations and SOD activity (Spearman’s ρ = −0.318, p < 0.001).Overall, these findings show that greater biochemical tobacco exposure was associated with higher oxidative lipid damage and lower antioxidant defence, while MDA and SOD were inversely related across the study population (Figure 2).

Table 2.

Associations of Serum Cotinine with Oxidative Stress Biomarkers and the Relationship Between MDA and SOD

Variable Pair (n=190)	n	Coefficient	p-value
MDA–SOD	190	ρ = −0.318	<0.001
Cotinine–MDA	190	ρ = 0.576	<0.001
Cotinine–SOD	190	r = −0.518	<0.001

Notes: ρ, Spearman’s rank correlation coefficient; r, Pearson correlation coefficient. Statistical significance was set at p < 0.05.

Abbreviations: MDA, malondialdehyde; SOD, superoxide dismutase.

Figure 2.

Correlation between MDA and SOD levels among smokers and non-smokers. Spearman correlation analysis showed a significant inverse association between MDA and SOD levels (rho = −0.318; 95% CI: −0.441 to −0.183; p < 0.001; N = 190). Triangles represent non-smokers (n = 95), whereas circles represent smokers (n = 95). The solid line represents the overall fitted trend across all participants.

Discussion

This study provides evidence of early disruption in oxidant–antioxidant balance among young adult smokers, supporting the concept that biological consequences of smoking emerge at a subclinical stage. Rather than reiterating group differences, the present findings contribute to an understanding of how relatively short-term and moderate smoking exposure may be associated with redox imbalance before the onset of clinically apparent disease. This early biological vulnerability highlights oxidative stress as a plausible early correlate of smoking-related biological risk.^14,15

From a mechanistic perspective, cigarette smoke is a well-recognised source of reactive oxygen and nitrogen species capable of overwhelming endogenous antioxidant defences. Persistent exposure to these oxidants may shift redox homeostasis toward a pro-oxidative state, resulting in lipid peroxidation and impairment of antioxidant enzymes.^16,17 Within this context, the concurrent alteration of lipid peroxidation markers and antioxidant enzyme activity supports the relevance of the MDA–SOD interplay as an integrated indicator of early redox disturbance rather than isolated biomarker changes.¹⁸

Importantly, the study population consisted of young adults with relatively short smoking histories and predominantly low-to-moderate nicotine dependence. The presence of measurable redox imbalance in this demographic underscores that oxidative stress is not confined to long-term or heavy smokers, but may already be detectable early in the smoking trajectory. This observation is consistent with recent evidence indicating that compensatory antioxidant responses during early exposure may be limited or transient, with enzymatic defences becoming compromised once oxidative burden exceeds adaptive capacity.^19,20

The inverse association between markers of lipid peroxidation and antioxidant activity further supports the notion of imbalance between oxidative lipid damage and antioxidant defence. Reduced antioxidant capacity may permit accumulation of reactive species, which in turn exacerbates oxidative damage, suggesting a biological pattern that may occur even in the absence of clinical symptoms. Such a model aligns with contemporary redox biology frameworks that conceptualise oxidative stress as a dynamic imbalance affecting cellular signalling and resilience rather than a binary pathological state.^21,22

From a preventive health perspective, these findings have important implications. Young adult smokers represent a population in which biological risk may accumulate silently, well before engagement with healthcare services for overt disease. However, because this was a cross-sectional biomarker study, the findings should be interpreted as evidence of early biological alteration rather than proof of future disease progression or healthcare burden.^23,24 In this context, biomarkers reflecting early oxidative imbalance may support preventive strategies, and strengthen awareness of early smoking-related biological effects in young populations.²⁵

The findings also reinforce the importance of early preventive action. Young adults frequently perceive light or moderate smoking as low risk, potentially delaying cessation efforts. Evidence of subclinical biological impact may therefore contribute to more effective health communication strategies, campus-based prevention programmes, and primary healthcare initiatives aimed at reducing future disease burden.²⁶

Several limitations should be acknowledged. First, the cross-sectional design limits causal inference and does not allow determination of the temporal relationship between smoking exposure and oxidant–antioxidant imbalance. Second, potential confounders such as dietary antioxidant intake, physical activity, socioeconomic status, and environmental exposures were not assessed or adjusted for, despite their known influence on oxidative stress biomarkers. These unmeasured factors may have influenced MDA and SOD levels and should therefore be considered when interpreting the observed differences between smokers and non-smokers. Future studies with longitudinal designs and more comprehensive assessment of lifestyle, socioeconomic, and environmental factors are needed to clarify the progression of smoking-related oxidative imbalance in young adults.

Conclusion

This study showed that young adult smokers exhibit early oxidant–antioxidant imbalance, reflecting subclinical biological vulnerability associated with smoking exposure even in the absence of overt disease. The observed disruption of the MDA–SOD interplay highlights that redox dysregulation may be detectable at an early stage of the smoking trajectory, before clinically apparent disease is present.

From a preventive health perspective, these findings underscore the importance of recognising young smokers as a priority population for early biological risk awareness and prevention-oriented efforts. However, because this study used a cross-sectional design, the findings should not be interpreted as evidence of disease progression or future healthcare burden. The identification of oxidant–antioxidant imbalance as an early biological signal supports the potential role of redox biomarkers in strengthening early assessment of smoking-related biological effects among young adults. Longitudinal studies are needed to determine whether this early redox imbalance predicts subsequent clinical outcomes.

Funding Statement

This research was supported by the Directorate of Research and Community Service, Universitas Brawijaya under grant number 00738.67/UN10.A0501/B/PT.01.03.2/2025.

Disclosure

The authors report no conflicts of interest in this work.