Abstract
Background: Alopecia areata (AA) is a T-cell-mediated autoimmune disease that significantly impacts patient quality of life. The breakdown of hair follicle immune privilege underlies AA pathogenesis. However, the precise mechanism of this breakdown remains unclear. This study investigates the potential role of reactive oxygen species in AA pathogenesis. Summary: A systematic review and meta-analysis were conducted on observational studies and randomized controlled trials from 2000 to 2024. Studies included AA patients and measured oxidative stress index (OSI), malondialdehyde (MDA), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), or paraoxonase-1 (PON1). Extracted data were analyzed using the Cochrane risk-of-bias tool and random-effects models. The review included 21 studies with 743 AA patients. OSI was elevated in AA patients (effect size = 1.58, 95% CI: 0.31–2.68, p = 0.00068). MDA levels were also elevated (effect size = 1.60, 95% CI: 0.43–2.6, p = 0.00023), while SOD (effect size = −0.97, 95% CI: −1.65 to −0.30, p = 0.00066) and GSH-Px (effect size = −1.41, 95% CI: −2.28 to −0.53, p = 0.00068) activities were reduced. PON1 levels showed no significant difference (effect size = −3.56, 95% CI: −8.63 to 1.51, p = 0.051). Key Messages: The elevated OSI and MDA, and decreased antioxidant activity in AA patients suggest a substantial role for reactive oxygen species and oxidative stress in AA pathogenesis, highlighting oxidative stress as a potential target for therapeutic intervention. These results underscore the importance of oxidative stress in AA and support further research into antioxidant-based therapies.
Introduction
Alopecia areata (AA) is a common autoimmune inflammatory non-scarring form of alopecia. This disease can manifest as patchy, diffuse, or complete alopecia involving the scalp or body and affects approximately 2% of the population. AA disease course varies considerably and impacts patient quality of life significantly [1‒3].
The current understanding suggests that AA arises from a disruption of hair follicle immune privilege [4]. This disruption allows autoreactive CD4+ and CD8+ T lymphocytes, in addition to natural killer cells, to target and destroy hair follicles in the anagen growth stage [4]. Several theories describe the mechanism by which intrinsic or extrinsic factors disrupt hair follicle immune privilege [2, 5, 6]. However, few theories describe the potentially significant involvement of reactive oxygen species (ROS) in AA pathogenesis.
ROS arise from the single-electron reduction of atmospheric oxygen and enable the maintenance of biological homeostasis. However, in excess their presence has been associated with oxidative-stress-derived autoimmune disease [7]. The oxidative stress index (OSI) is a composite measure that provides a comprehensive assessment of oxidative stress by calculating the ratio of total oxidants to total antioxidants in serum. An elevation of this marker reflects an imbalance between ROS production and antioxidant defense mechanisms [8]. Oxidative stress levels may also be indirectly quantified by measuring lipid peroxide levels, including malondialdehyde (MDA), which arise from reactions between ROS and polyunsaturated fatty acids [9].
ROS levels are controlled by antioxidants, which scavenge free radicals or convert them to less reactive forms. Common antioxidant enzymes include superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), and paraoxonase (PON1) [10]. Oxidative stress often results from excess ROS production or insufficient antioxidant function [10]. Recent evidence additionally suggests that the acquisition of noncanonical functions in antioxidant enzymes such as SOD1 can occur through interaction with mutant enzymes and enhance antioxidative activity, implying new investigative paths for understanding the accumulation of ROS [11].
A systematic review and meta-analysis were conducted of the available literature measuring the presence of oxidative stress markers, lipid peroxides, and antioxidants in AA patients. By quantifying a relationship between the presence of oxidative stress in healthy control individuals and in patients with AA, this review aims to create a better understanding of the potential role of ROS in the breakdown of hair follicle immune privilege and the subsequent development of AA.
Methods
Search Strategy
This review was not registered in any database, and a protocol was not prepared prior to conducting the review. A literature search was conducted adhering to PRISMA guidelines (online suppl. Table 1; for all online suppl. material, see https://doi.org/10.1159/000543373). A literature search was conducted using MEDLINE (via PubMed), Embase (via University of Toronto Libraries), and the Cochrane Central Register of Controlled Trials (CENTRAL) from 2000 to June 2024. The search focused on observational studies (case control, cohort) and randomized controlled trials published in English during this period. Exclusions were made for reviews, case reports, editorials, conference abstracts, and animal studies. To ensure comprehensive coverage of the topic, the search strategy included Medical Subject Headings (MeSH) terms, Embase Emtree terms, and relevant keywords related to AA, oxidative stress, antioxidants, and specific biomarkers of interest. Boolean operators were applied to refine search results. The following terms and concepts were combined to construct the final search query:
Alopecia Areata
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MeSH/Emtree terms: “Alopecia Areata”
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Keywords: “alopecia areata,” “AA,” “hair loss,” “autoimmune alopecia”
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Reactive Oxygen Species and Oxidative Stress
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MeSH/Emtree terms: “Reactive Oxygen Species,” “Oxidative Stress”
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Keywords: “reactive oxygen species,” “oxidative stress,” “ROS”
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Antioxidants and Enzymes
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MeSH/Emtree terms: “Antioxidants,” “Superoxide Dismutase,” “Glutathione Peroxidase,” “Paraoxonase”
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Keywords: “antioxidants,” “superoxide dismutase,” “SOD,” “glutathione peroxidase,” “GSH-Px,” “paraoxonase,” “PON1”
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Oxidative Stress Biomarkers
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Keywords related to biomarkers of oxidative stress, specifically targeting malondialdehyde (MDA) and oxidative stress index (OSI).
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Keywords: “malondialdehyde,” “MDA,” “oxidative stress index,” “OSI”
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The terms were combined as follows: [(“Alopecia Areata”[MeSH] OR “alopecia areata” OR “AA” OR “autoimmune alopecia” OR “hair loss”) AND (“Reactive Oxygen Species”[MeSH] OR “oxidative stress” OR “ROS”) AND (“Antioxidants”[MeSH] OR “superoxide dismutase” OR “SOD” OR “glutathione peroxidase” OR “GSH-Px” OR “paraoxonase” OR “PON1”) AND (“malondialdehyde” OR “MDA” OR “oxidative stress index” OR “OSI”)].
Eligibility Criteria
Studies were included if they involved human participants with any subtype of AA and examined the role of ROS in the development, progression, or pathogenesis of AA. Eligible studies measured OSI, MDA, SOD, GSH-Px, or PON1 in serum, erythrocytes, or both. Other measurement parameters, such as advanced glycation end products and ischemic-modified albumin, were excluded because they are valuable as downstream biomarkers of oxidative stress but not as direct agents in oxidative modulation or pathogenesis [12, 13].
Study Selection
Following de-duplication, Covidence was used by two reviewers (J.P. and S.F.) who independently screened titles and abstracts for eligibility to arrive at an agreement with the involvement of a third reviewer if needed. Full-text articles of potentially eligible studies were then retrieved and independently assessed by the same two reviewers using a standardized data extraction form.
Data Extraction
Two reviewers (P.A. and E.T.) independently used Mircosoft Excel to extract relevant data from the included studies using a standardized form. Information collected included study characteristics (author list, year, design, and total participants), participant characteristics (AA patients, control patients, and control type), and parameters measured (OSI, MDA, SOD, GSH-Px, or PON1 in serum or erythrocytes).
Risk of Bias Assessment
The risk of bias in each included study was independently assessed by two reviewers (J.P. and P.A.) using version 2 of the Cochrane tool for assessing risk of bias in randomized trials (RoB 2 tool) [14]. This tool evaluated bias arising from the randomization process, bias due to deviations from intended interventions, bias due to missing outcome data, bias in measurement of the outcome, bias in selection of the reported result, and overall bias. Each domain was rated as low risk, some concerns, or high risk of bias. Disagreements were resolved through discussion and, if necessary, with the involvement of a third reviewer.
Data Analysis
tThe standardized mean difference (Cohen’s d) for each included study and for pooled data across each parameter between AA and control patients was calculated. The Meta-Essentials tool, an open-source, Excel-based software developed by Suurmond et al. [15], to facilitate calculations such as Cohen’s d, forest plots, and more was used. Confidence intervals (CI), z-statistic, and p values were also computed, with a p value of less than 0.05 deemed statistically significant. Due to variability in study designs and participant characteristics, random-effects models to determine meta-analytic effect sizes and CI were opted for, allowing for broader generalization beyond the included studies. Heterogeneity among the studies was evaluated using the chi-square test and quantified with the I2 statistic, where values exceeding 50% indicated significant heterogeneity beyond random chance.
Results
Search Results, Study Characteristics, and Bias
The literature search yielded a total of 167 articles. After the initial title and abstract screening, 64 articles remained that investigated the presence of ROS, the number of antioxidant enzymes, and oxidative stress markers in AA patients. The PRISMA flow diagram depicting the article selection, screening, and eligibility selection is depicted in Figure 1. Following the full-text screening, 21 articles, encompassing a total of 743 AA patients, were included for analysis (Table 1). Out of 21 articles, 20 had some concern for bias while one had low risk of bias (Fig. 2, 3, 4a–c).
Flow diagram of literature screening using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (figure adapted from http://prisma-statement.org).
Flow diagram of literature screening using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (figure adapted from http://prisma-statement.org).
Characteristics of included studies identified through systematic review organized by year of publication
Study . | Year . | Design . | Total participants . | AA . | Control . | Control description . | Parameter(s) . |
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Naziroglu and Kokcam [16] | 2000 | Case control | 71 | 37 | 34 | Healthy age and sex matched | Serum – GSH-Px |
Erythrocyte – GSH-Px | |||||||
Akar et al. [17] | 2002 | Case control | 20 | 10 | 10 | Healthy age and sex matched | Tissue – SOD, GSH-Px |
Koca et al. [18] | 2005 | Case control | 44 | 24 | 20 | Healthy age and sex matched | Serum – MDA, SOD |
Abdel Fattah et al. [19] | 2011 | Case control | 75 | 50 | 25 | Healthy age, gender, and BMI matched | Serum – MDA |
Erythrocyte – SOD | |||||||
Amirnia et al. [20] | 2011 | Case control | 54 | 27 | 27 | Healthy age and sex matched | Serum – MDA, SOD, GSH-Px |
Bilgili et al. [21] | 2013 | Case control | 78 | 39 | 39 | Healthy age and sex matched | Serum – OSI, PON1 |
Ramadan et al. [22] | 2013 | Case control | 30 | 15 | 15 | Healthy age and sex matched | Serum – PON1 |
Bakry et al. [23] | 2014 | Case control | 65 | 35 | 30 | Healthy age and sex matched | Serum – OSI, MDA |
Motor et al. [24] | 2014 | Case control | 82 | 46 | 36 | Healthy age and sex matched | Serum – OSI |
Rasheed et al. [25] | 2014 | Case control | 51 | 21 | 30 | Healthy age, sex, and ethnicity matched | Serum – SOD |
Yenin et al. [26] | 2015 | Case control | 124 | 62 | 62 | Healthy age and sex matched | Serum – MDA |
Erythrocyte – SOD, GSH-Px | |||||||
Öztürk et al. [27] | 2018 | Case control | 60 | 30 | 30 | Healthy | Tissue – MDA, SOD |
Cwynar et al. [10] | 2018 | Case control | 60 | 30 | 30 | Healthy | Erythrocyte – SOD, GSH-Px |
Cwynar et al. [28] | 2018 | Case control | 48 | 24 | 24 | Healthy age and sex matched | Serum – MDA |
Ataş et al. [12] | 2019 | Case control | 120 | 60 | 60 | Healthy age and sex matched | Serum – SOD, MDA |
Cwynar et al. [28] | 2019 | Case control | 60 | 30 | 30 | Healthy age and sex matched | Serum – MDA, PON1 |
Dizen-Namdar et al. [29] | 2019 | Case control | 110 | 60 | 50 | Healthy age and sex matched | Serum – OSI, PON1 |
Cakirca et al. [30] | 2020 | Case control | 66 | 33 | 33 | Healthy age and sex-matched | Serum – OSI |
Sachdeva et al. [31] | 2021 | Cross-sectional | 80 | 40 | 40 | Healthy age and sex matched | Serum – SOD, MDA |
Bakry et al. [32] | 2022 | Case control | 60 | 30 | 30 | Healthy age and sex matched | Serum – OSI |
Shakoei et al. [13] | 2023 | Cross-sectional | 80 | 40 | 40 | Healthy age and sex matched | Serum – PON1 |
Study . | Year . | Design . | Total participants . | AA . | Control . | Control description . | Parameter(s) . |
---|---|---|---|---|---|---|---|
Naziroglu and Kokcam [16] | 2000 | Case control | 71 | 37 | 34 | Healthy age and sex matched | Serum – GSH-Px |
Erythrocyte – GSH-Px | |||||||
Akar et al. [17] | 2002 | Case control | 20 | 10 | 10 | Healthy age and sex matched | Tissue – SOD, GSH-Px |
Koca et al. [18] | 2005 | Case control | 44 | 24 | 20 | Healthy age and sex matched | Serum – MDA, SOD |
Abdel Fattah et al. [19] | 2011 | Case control | 75 | 50 | 25 | Healthy age, gender, and BMI matched | Serum – MDA |
Erythrocyte – SOD | |||||||
Amirnia et al. [20] | 2011 | Case control | 54 | 27 | 27 | Healthy age and sex matched | Serum – MDA, SOD, GSH-Px |
Bilgili et al. [21] | 2013 | Case control | 78 | 39 | 39 | Healthy age and sex matched | Serum – OSI, PON1 |
Ramadan et al. [22] | 2013 | Case control | 30 | 15 | 15 | Healthy age and sex matched | Serum – PON1 |
Bakry et al. [23] | 2014 | Case control | 65 | 35 | 30 | Healthy age and sex matched | Serum – OSI, MDA |
Motor et al. [24] | 2014 | Case control | 82 | 46 | 36 | Healthy age and sex matched | Serum – OSI |
Rasheed et al. [25] | 2014 | Case control | 51 | 21 | 30 | Healthy age, sex, and ethnicity matched | Serum – SOD |
Yenin et al. [26] | 2015 | Case control | 124 | 62 | 62 | Healthy age and sex matched | Serum – MDA |
Erythrocyte – SOD, GSH-Px | |||||||
Öztürk et al. [27] | 2018 | Case control | 60 | 30 | 30 | Healthy | Tissue – MDA, SOD |
Cwynar et al. [10] | 2018 | Case control | 60 | 30 | 30 | Healthy | Erythrocyte – SOD, GSH-Px |
Cwynar et al. [28] | 2018 | Case control | 48 | 24 | 24 | Healthy age and sex matched | Serum – MDA |
Ataş et al. [12] | 2019 | Case control | 120 | 60 | 60 | Healthy age and sex matched | Serum – SOD, MDA |
Cwynar et al. [28] | 2019 | Case control | 60 | 30 | 30 | Healthy age and sex matched | Serum – MDA, PON1 |
Dizen-Namdar et al. [29] | 2019 | Case control | 110 | 60 | 50 | Healthy age and sex matched | Serum – OSI, PON1 |
Cakirca et al. [30] | 2020 | Case control | 66 | 33 | 33 | Healthy age and sex-matched | Serum – OSI |
Sachdeva et al. [31] | 2021 | Cross-sectional | 80 | 40 | 40 | Healthy age and sex matched | Serum – SOD, MDA |
Bakry et al. [32] | 2022 | Case control | 60 | 30 | 30 | Healthy age and sex matched | Serum – OSI |
Shakoei et al. [13] | 2023 | Cross-sectional | 80 | 40 | 40 | Healthy age and sex matched | Serum – PON1 |
Forest plot of effect sizes (Cohen’s d) for studies measuring oxidative stress index (OSI). The risk of bias categorizes potential sources of bias in each study as follows: “R” indicates bias arising from the randomization process, “D” represents bias due to deviations from intended interventions, “Mi” refers to bias resulting from missing outcome data, “Me” stands for bias in the measurement of the outcome, “S” denotes bias in the selection of the reported result, and “O” summarizes the overall risk of bias. The judgment symbols used to assess the level of bias are as follows: a red “X” indicates a high risk of bias, a green “+” represents a low risk of bias, and a blue question mark “?” indicates some concern regarding the risk of bias.
Forest plot of effect sizes (Cohen’s d) for studies measuring oxidative stress index (OSI). The risk of bias categorizes potential sources of bias in each study as follows: “R” indicates bias arising from the randomization process, “D” represents bias due to deviations from intended interventions, “Mi” refers to bias resulting from missing outcome data, “Me” stands for bias in the measurement of the outcome, “S” denotes bias in the selection of the reported result, and “O” summarizes the overall risk of bias. The judgment symbols used to assess the level of bias are as follows: a red “X” indicates a high risk of bias, a green “+” represents a low risk of bias, and a blue question mark “?” indicates some concern regarding the risk of bias.
Forest plot of effect sizes (Cohen’s d) for studies measuring malondialdehyde (MDA) levels. The risk of bias categorizes potential sources of bias in each study as follows: “R” indicates bias arising from the randomization process, “D” represents bias due to deviations from intended interventions, “Mi” refers to bias resulting from missing outcome data, “Me” stands for bias in the measurement of the outcome, “S” denotes bias in the selection of the reported result, and “O” summarizes the overall risk of bias. The judgment symbols used to assess the level of bias are as follows: a red “X” indicates a high risk of bias, a green “+” represents a low risk of bias, and a blue question mark “?” indicates some concern regarding the risk of bias.
Forest plot of effect sizes (Cohen’s d) for studies measuring malondialdehyde (MDA) levels. The risk of bias categorizes potential sources of bias in each study as follows: “R” indicates bias arising from the randomization process, “D” represents bias due to deviations from intended interventions, “Mi” refers to bias resulting from missing outcome data, “Me” stands for bias in the measurement of the outcome, “S” denotes bias in the selection of the reported result, and “O” summarizes the overall risk of bias. The judgment symbols used to assess the level of bias are as follows: a red “X” indicates a high risk of bias, a green “+” represents a low risk of bias, and a blue question mark “?” indicates some concern regarding the risk of bias.
Forest plot of effect sizes (Cohen’s d) for studies measuring superoxide dismutase (SOD) levels (a), glutathione peroxidase (GSH-Px) levels (b), and paraoxonase-1 (PON1) levels (c). The risk of bias categorizes potential sources of bias in each study as follows: “R” indicates bias arising from the randomization process, “D” represents bias due to deviations from intended interventions, “Mi” refers to bias resulting from missing outcome data, “Me” stands for bias in the measurement of the outcome, “S” denotes bias in the selection of the reported result, and “O” summarizes the overall risk of bias. The judgment symbols used to assess the level of bias are as follows: a red “X” indicates a high risk of bias, a green “+” represents a low risk of bias, and a blue question mark “?” some concern regarding the risk of bias.
Forest plot of effect sizes (Cohen’s d) for studies measuring superoxide dismutase (SOD) levels (a), glutathione peroxidase (GSH-Px) levels (b), and paraoxonase-1 (PON1) levels (c). The risk of bias categorizes potential sources of bias in each study as follows: “R” indicates bias arising from the randomization process, “D” represents bias due to deviations from intended interventions, “Mi” refers to bias resulting from missing outcome data, “Me” stands for bias in the measurement of the outcome, “S” denotes bias in the selection of the reported result, and “O” summarizes the overall risk of bias. The judgment symbols used to assess the level of bias are as follows: a red “X” indicates a high risk of bias, a green “+” represents a low risk of bias, and a blue question mark “?” some concern regarding the risk of bias.
Oxidative Stress Index
Six studies, encompassing a total of 243 AA patients, examined the OSI. All but one study reported higher OSI levels in AA patients compared to healthy controls (Fig. 2). A meta-analysis of these six studies demonstrated a significant effect size difference between AA patients and healthy controls (effect size = 1.58, 95% CI: 0.31–2.68, p = 0.00068).
Lipid Hydroperoxide Levels
Nine studies involving 352 AA patients assessed MDA levels. All studies consistently showed elevated MDA levels in AA patients when compared to healthy controls (Fig. 3). An analysis of the cumulative effect size of these studies indicated a significant difference between the two groups (effect size = 1.60, 95% CI: 0.43–2.6, p = 0.00023).
Antioxidant Enzyme Activity
A total of 8 studies, comprising 314 AA patients, evaluated SOD levels. Of these, seven studies reported lower SOD activity in AA patients compared to healthy controls (Fig. 4a). The meta-analysis revealed a significant effect size difference between the groups (effect size = −0.97, 95% CI: −1.65 to −0.30, p = 0.00066).
Four studies, involving 156 AA patients, assessed GSH-Px levels. Each study consistently demonstrated a reduction in GSH-Px levels among AA patients in comparison to healthy controls (Fig. 4b). The pooled analysis showed a significant effect size difference between the two populations (effect size = −1.41, 95% CI: −2.28 to −0.53, p = 0.00068).
Five studies, including 184 AA patients, investigated PON1 levels. While all studies indicated decreased PON1 levels in AA patients compared to healthy controls (Fig. 4c), the meta-analysis did not reveal a statistically significant effect size difference between the groups (effect size = −3.56, 95% CI: −8.63 to 1.51, p = 0.051).
Discussion
ROS and Oxidative Stress in AA
Our comprehensive review underscores the pivotal role of ROS and oxidative stress in the pathogenesis of AA. A total of 6 studies investigating serum OSI were reviewed, which included 243 patients with AA (Table 1). The cumulative effect size (Cohen’s d) for these studies was 1.58 (95% CI: 0.31, 2.86; Fig. 2), suggesting a significantly higher mean serum OSI among AA patients when compared to controls. These findings are consistent with previous studies suggesting that elevated ROS concentration leads to activation of MAPK, activator protein-1, and nuclear factor kappa beta (NF-κB) pathways. Locally, these pathways could promote the synthesis of proinflammatory cytokines, including TNF-α and IL-1β, by macrophages and dendritic cells [33]. Subsequent recruitment of B and T cells to the site of immune action further increases localized ROS production, triggering a ROS-mediated cycle of aberrant immune responses [33, 34].
Lipid Peroxidation and Its Role in AA Pathogenesis
Nine studies were reviewed that quantified serum MDA levels, which included 352 AA patients (Table 1). The cumulative effect size (Cohen’s d) for these studies was 1.60 (95% CI: 0.43–2.6; Fig. 3), suggesting significantly higher mean serum MDA levels among AA patients when compared to controls. These findings suggest that lipid peroxidation is a potential contributor to AA etiology. Lipid hydroperoxides exacerbate oxidative stress and potentially trigger inflammatory immune responses as these oxidized lipids may be perceived as pathogens by the body’s immune system [19, 23, 27, 28, 31, 35]. Consequently, these autoreactive immune cells may be activated against hair follicle antigens. Lipid hydroperoxides are also used to form carbonylated protein adducts thought to be involved in autoimmune sensitization [9].
Antioxidant Enzyme Activity in AA Patients
The review also revealed a deficiency in antioxidant enzyme activity in individuals with AA. Among 8 studies and 314 AA patients, SOD activity was significantly reduced (effect size = −0.97, 95% CI: −1.65 to −0.30; Fig. 4a) when compared to controls. Within 4 studies and 156 AA patients, GSH-Px activity was also significantly reduced (effect size = −1.41, 95% CI: −2.28 to −0.53; Fig. 4b) in AA patients. These results suggest that decreased antioxidant enzyme activity likely contributes as a factor underlying the markedly increased oxidative stress observed in AA patients. Interestingly, among 5 studies investigating 184 AA patients, there was no significant difference in PON1 levels when compared to controls (effect size = −3.56, 95% CI: −8.63 to 1.51; Fig. 4c). This finding suggests that PON1 deficiency may play a less central role in ROS accumulation when compared to other antioxidant enzymes. Antioxidant deficiency among AA patients also renders antioxidant supplementation a promising therapeutic modality in AA management. Supplementation with these antioxidants has shown promise in preliminary studies, indicating their potential to reduce oxidative stress and hair follicle damage [16, 36‒39]. Clinical trials focusing on these supplements are crucial to establishing their efficacy and optimal dosages for AA treatment.
Limitations and Future Directions
While our review provides a robust synthesis of the available literature on ROS and oxidative stress in AA, it is not without limitations. The diversity in study results, particularly concerning the insignificant deviation in PON1 activity among AA patients relative to controls, emphasizes the need for more research investigating the role of antioxidants in AA pathogenesis. There was also some concern for bias in 20/21 of the studies analyzed, which was derived in all cases by a high potential for selection bias and a moderate potential for detection bias. To minimize the potential for bias, future studies should randomize and conceal patient allocations while ensuring outcome assessment is blinded. Moreover, there is a significant potential for future studies to explore how ROS-related pathophysiological processes may be targeted not only in AA but also in other dermatological conditions characterized by similar oxidative stress mechanisms, such as atopic dermatitis and vitiligo [1, 2]. Additionally, we have earlier noted the intriguing findings of Sun and Lei [11] related to protein-protein interactions and the noncanonical role of antioxidant enzymes like SOD1. Should such phenomena apply in AA pathogenesis, then this would suggest new avenues for research that could yield significant insights into the molecular underpinnings of the disease. Investigations in this direction would therefore be logical additions to potential therapeutic options, as suggested by Paus et al. [4]. Future studies should also investigate articles in languages other than English while using appropriate translation resources, potentially expanding the sample size and generalizability of the current findings.
Conclusion
The evidence amassed from this comprehensive review strongly advocates for the role of ROS and oxidative stress in the pathogenesis of AA. This suggests a promising therapeutic avenue through antioxidant supplementation and other strategies targeting oxidative pathways to achieve local immunomodulation. The field is ripe for future exploratory studies to validate these findings and to expand the scope of treatment options for AA, potentially improving outcomes for patients suffering from this challenging condition.
Statement of Ethics
A Statement of Ethics is not applicable because this study is based exclusively on published literature.
Conflict of Interest Statement
The authors declare no conflict of interest.
Funding Sources
This research received no external funding.
Author Contributions
Conceptualization: J.P.; methodology: J.P. and P.P.A.; software: P.A. and E.T.; validation: P.P.A. and M.E.; formal analysis, data curation, and writing – review and editing: S.F., B.Y., and P.P.A.; investigation and writing – original draft preparation: J.P. and M.E.; resources, supervision, and project administration: M.W. and M.E.; visualization: M.W.; and funding acquisition: N/A.
Data Availability Statement
The data supporting the findings of this systematic review are derived from publicly available sources. Data extracted from the included studies are available within the article and its supplementary materials. Additional relevant data are accessible via the corresponding hyperlinks to the open-access databases and repositories as cited within the references section of this paper. No proprietary or nonpublic data were used to support the conclusions of this review. Further inquiries can be directed to the corresponding author.