Abstract
Objective:
To examine the associations between superoxide dismutase (SOD) levels and the burden of cerebral small vessel disease (CSVD), as well as vascular mild cognitive impairment (VaMCI), in elderly patients.
Methods:
In this study, a cohort of 286 elderly individuals was included. Each participant received a comprehensive cognitive assessment. Plasma SOD levels were evaluated. The cumulative CSVD burden was quantified using an ordinal scale ranging from 0 to 4, based on four key imaging indicators of CSVD: white matter hyperintensity, deep cerebral microbleeds, lacunes, and enlarged perivascular spaces in the basal ganglia. To evaluate the associations among SOD levels, CSVD burden, and cognitive function, we employed binary logistic regression analysis, tests for trend, receiver operating characteristic curve analysis, and mediation analysis.
Results:
After adjusting for potential confounders, multivariate logistic regression analysis revealed that elevated levels of SOD were significantly associated with a reduced risk of VaMCI (OR: 0.919; 95% CI: 0.896–0.943; P < 0.001) and a lower likelihood of severe CSVD burden (OR: 0.980; 95% CI: 0.968–0.992; P = 0.001) in elderly patients. Compared with the lowest quartile of SOD levels, the OR for VaMCI in the highest quartile of SOD levels was 0.039 (95% CI: 0.016–0.100; P for trend < 0.001) after adjusting for potential confounders. For severe CSVD burden, the corresponding OR was 0.442 (95% CI: 0.214–0.913; P for trend = 0.011). Mediation analysis revealed that the severe CSVD burden significantly moderated the relationship between SOD levels and VaMCI.
Conclusion:
Elevated SOD levels serve as a protective factor against severe CSVD burden and VaMCI in elderly patients. A portion of the protective effect of increased SOD on VaMCI may be attributed to its role in mitigating the severity of CSVD.
Introduction
Vascular cognitive impairment (VCI) refers to a broad spectrum of vascular brain abnormalities that lead to any degree of cognitive decline, from mild vascular cognitive impairment (VaMCI) to vascular dementia (VaD) (Mok et al., 2024; Iadecola et al., 2019). VaMCI is widely recognized as an early and potentially reversible stage of VaD (Iadecola et al., 2019; Lissek and Suchan, 2021). Despite the substantial impact of VCI on public health and the economy, the intricate nature of its pathogenesis has hindered the development of sufficiently effective therapeutic interventions (Mok et al., 2024; Rundek et al., 2022).
Preclinical and clinical research findings suggest that cerebral small vessel disease (CSVD) is the primary pathology associated with VCI (Mok et al., 2024; Inoue et al., 2023). Traditionally, structural magnetic resonance imaging (MRI) has been used to visualize tissue damage related to CSVD, which may manifest as lacunes of presumed vascular origin, white matter hyperintensities (WMH), perivascular spaces (PVS), and cerebral microbleeds (CMBs) (Duering et al., 2023; Wardlaw et al., 2013). The total CSVD burden score, derived from a visual rating of these imaging markers, including lacunes, WMH, PVS, and CMBs, exhibits strong construct validity and more accurately reflects the severity of CSVD burden (Duering et al., 2023; Huijts et al., 2013).
Oxidative stress, characterized by an overabundance of reactive oxygen species (ROS) and other reactive molecules, plays a crucial role in the pathological mechanisms underlying VCI (He et al., 2024; Luca and Luca, 2019). Within the context of VCI, oxidative stress can induce endothelial damage, which represents an early event in the progression of this condition (Luca and Luca, 2019). Interestingly, oxidative stress has also been shown to be significantly implicated in the pathogenesis of CSVD (Hannawi, 2024; Grochowski et al., 2018; De Silva and Miller, 2016). In healthy physiological conditions, living organisms sustain a stable redox homeostasis. Endogenous antioxidants within cells, such as superoxide dismutase (SOD), play a critical role in scavenging excess ROS and alleviating oxidative stress-induced damage (Nolfi-Donegan et al., 2020).
SOD serves as an enzymatic antioxidant that catalyzes the conversion of superoxide radicals to hydrogen peroxide within cells, thereby playing a crucial role in antioxidant stress (Islam et al., 2022). SOD deficiency is implicated in the pathogenesis of several neurological disorders, including cognitive impairment and CSVD (Balendra and Singh, 2021; Zhu et al., 2019). In the context of VCI, CSVD and oxidative stress are widely recognized as significant contributors to cognitive decline (Mok et al., 2024; Luca and Luca, 2019). SOD plays a crucial role in mitigating the detrimental effects of ROS, which are generated during events such as inflammation and hypoperfusion injury, both of which are closely associated with CSVD (Hannawi, 2024; Quick et al., 2021). Given the cytoprotective benefits of SOD, significant attention has been directed toward exploring its potential as a biomarker for CSVD and VCI. However, limited research has investigated the association between SOD levels and CSVD burden as well as VaMCI.
Considering the critical role of oxidative stress and microvascular dysfunction in VCI, this study aimed to explore whether SOD is associated with a reduced risk of severe CSVD burden or VaMCI. Additionally, the research examined if the CSVD burden acts as a mediator in the relationship between SOD and VaMCI in elderly patients.
Materials and methods
Participants
This retrospective analysis encompassed 286 eligible inpatients consecutively admitted to the Department of Neurology at Hebei General Hospital from August 2019 to April 2022. Inclusion criteria were as follows: (1) age 60 years or older; (2) availability of complete MRI scans, including T1-weighted imaging, T2-weighted imaging, fluid-attenuated inversion recovery, and susceptibility-weighted imaging; and (3) completion of comprehensive cognitive function assessments. Patients were excluded from the study if they had dementia or other conditions that could potentially confound the assessment of cognitive function, such as an acute cerebrovascular event (defined as a clinically diagnosed ischemic stroke or intracerebral hemorrhage occurring within 30 days prior to the assessment), traumatic brain injuries, schizophrenia, hypothyroidism, anxiety disorders, depressive disorders, epileptic disorders, or metabolic encephalopathy.
Collection of demographic, clinical, and laboratory data
The demographic and clinical data, including age, sex, education level, body mass index (BMI), smoking status, alcohol consumption, blood pressure measurements (both systolic and diastolic), and prior medical history (stroke, diabetes, hypertension, and coronary heart disease), were collected from electronic hospital records or patient interviews. “Current drinking” was defined as the self-reported consumption of any alcoholic beverage at least once per week in the past year.
Laboratory examinations were conducted following an overnight fast of 8 h. The tests included measurements of fasting plasma glucose, total cholesterol (TC), triglyceride, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), lipoprotein(a), uric acid, fibrinogen, serum homocysteine, and SOD. Specifically, plasma SOD activity was determined using the pyrogallol autoxidation method with a commercial kit (Autobio Diagnostics, Catalog No. SOD000G). All biochemical analyses were performed on a TBA-FX8 fully automated biochemical analyzer (Canon Medical Systems). The complete specifications of reagent kits are listed in Supplementary material 1.
Evaluation of CSVD burden
To evaluate the imaging markers of CSVD, a 3.0 Tesla brain MRI (Sigma, GE Healthcare, United States) was utilized. The comprehensive imaging protocol included T1-weighted imaging, T2-weighted imaging, fluid-attenuated inversion recovery, susceptibility-weighted imaging, and diffusion-weighted imaging. The characterization of these imaging markers was conducted in accordance with the Standards for Reporting Vascular Changes on Neuroimaging 2 (STRIVE-2) (Duering et al., 2023). A total CSVD score was determined on an ordinal scale from 0 to 4, incorporating four key imaging markers: lacunes, WMH, PVS, and CMBs (Duering et al., 2023; Huijts et al., 2013). The Fazekas rating scale was employed to assess the severity of periventricular and deep WMH (Fazekas et al., 1993). The Microbleed Anatomical Rating Scale was utilized to evaluate CMBs (Gregoire et al., 2009). The number of PVS or lacunes was documented. A score of one point was assigned for each of the following criteria: a Fazekas score of 3 for periventricular WMH or ≥ 2 for deep WMH, the presence of at least one lacune, one or more deep CMBs, and a count of ≥ 11 PVS in the basal ganglia. The total CSVD score was used to evaluate the overall burden of CSVD (Huijts et al., 2013; Staals et al., 2015). Total CSVD scores exceeding 2 were classified as indicative of severe CSVD burden (Kim et al., 2021; Teng et al., 2022).
Assessment of vascular mild cognitive impairment
In this study, all patients underwent a comprehensive series of neuropsychological assessments. These evaluations included the standardized Chinese (Beijing) version of the Montreal Cognitive Assessment (MoCA), assessments of both basic and instrumental activities of daily living (ADL and IADL), the 14-item Hamilton Anxiety Rating Scale (HAMA), and the 24-item Hamilton Depression Rating Scale (HAMD). Cognitive impairment was objectively confirmed based on the following criteria from neuropsychological tests: MoCA scores ( ≤ 13 for individuals without formal education, ≤ 19 for those with 1–6 years of schooling, and ≤ 24 for those with 7 or more years of education), HAMA score < 6, and HAMD score < 8 (Lu et al., 2011). The diagnosis of VaMCI was established according to previously published criteria. These criteria included: (a) Objective evidence of cognitive decline; (b) clinical features consistent with vascular etiology; (c) sufficient cerebrovascular disease to account for the cognitive deficits; and (d) absence of dementia, as indicated by normal performance in basic ADL and an IADL score below 10 (Skrobot et al., 2018; van der Flier et al., 2018).
Statistical analysis
Continuous variables were summarized using mean and standard deviation or median and interquartile range, depending on the distribution of data. Categorical variables were presented as proportions or percentages. For continuous variables, comparisons were carried out using Mann-Whitney U tests or independent samples t-tests based on the normality of data distribution. Categorical variables were analyzed using χ2 tests. Variables with a P-value < 0.1 in the initial univariate analyses were incorporated into multivariate binary logistic regression models to identify independent risk factors for VaMCI or severe CSVD burden. To evaluate trends, SOD levels were categorized into quartiles. The receiver operating characteristic (ROC) curve for SOD levels was constructed, and the optimal cut-off point was determined based on the maximum Youden Index. Statistical significance was set at P < 0.05. All statistical analyses were performed using SPSS version 26.0 (IBM Corporation, Armonk, NY).
For mediation analysis, R version 4.1.0 (R Foundation for Statistical Computing, Vienna, Austria) was utilized with the bruceR and mediation packages. This analysis aimed to assess whether severe CSVD burden mediated the relationship between SOD levels and VaMCI. The software automatically selected the appropriate model type and performed mean centering before building the model. Each analysis utilized 5,000 bootstrap samples to ensure robust effect estimation.
Results
Participants characteristics
This retrospective study included 286 eligible participants, consisting of 126 individuals diagnosed with VaMCI and 160 with normal cognitive function. The median age of the participants was 69 years, with 54.5% being male. The prevalence of VaMCI among elderly patients was 44.1%. Table 1 summarizes the characteristics of patients in both groups. Compared to those with normal cognitive function, participants in the VaMCI group were significantly older and had lower educational levels (P < 0.05). Additionally, the VaMCI group exhibited a higher frequency of severe CSVD burden compared to the normal cognitive group (P < 0.05). Regarding biochemical markers, individuals in the VaMCI group showed elevated levels of fibrinogen and serum homocysteine, while they had reduced levels of HDL-C, ApoA1, and SOD (P < 0.05).
TABLE 1
| Variable | Total (n = 286) |
Normal cognitive group (n = 160) |
VaMCI group (n = 126) |
P-value | Effect size (95% CI) |
|---|---|---|---|---|---|
| Age, median (IQR), year | 69 (65–74) | 68 (64–73) | 71 (67–75) | < 0.001* | ΔM: 3.00 (1.00, 4.00) |
| Sex (male), n (%) | 156 (54.5) | 81 (50.6) | 75 (59.5) | 0.133 | φ: 0.09 (–0.03, 0.20) |
| Education, median (IQR), year | 9 (6–12) | 9 (6–12) | 9 (6–11) | 0.018* | ΔM: 0.00 (0.00, 3.00) |
| BMI, median (IQR), kg/m2 | 25.3 (23.2–27.3) | 25.4 (23.2–27.3) | 25.2 (23.2–27.8) | 0.933 | ΔM: 0.02 (–0.77, 0.82) |
| Current smoking, n (%) | 53 (18.5) | 27 (16.9) | 26 (20.6) | 0.417 | ΔM: 2.00 (–3.00, 7.00) |
| Current drinking, n (%) | 41 (14.3) | 25 (15.6) | 16 (12.7) | 0.483 | ΔM: 0.00 (–3.00, 2.00) |
| Hypertension, n (%) | 196 (68.5) | 111 (69.4) | 85 (67.5) | 0.729 | φ: 0.05 (–0.07, 0.16) |
| SBP, median (IQR), mmHg | 140 (127–152) | 138 (126–152) | 143 (127–152) | 0.408 | φ: 0.04 (–0.08, 0.16) |
| DBP, median (IQR), mmHg | 81 (74–88) | 81 (74–88) | 80 (75–87) | 0.785 | φ: 0.02 (–0.10, 0.14) |
| Diabetes, n (%) | 84 (29.4) | 42 (26.3) | 42 (33.3) | 0.192 | φ: 0.08 (–0.04, 0.19) |
| Coronary heart disease, n (%) | 59 (20.6) | 34 (21.3) | 25 (19.8) | 0.770 | φ: 0.02 (–0.10, 0.13) |
| History of stroke, n (%) | 115 (40.2) | 58 (36.3) | 57 (45.2) | 0.124 | φ: 0.09 (–0.03, 0.21) |
| Fasting plasma glucose, median (IQR), mmol/L | 5.08 (4.60–6.00) | 5.08 (4.59–5.86) | 5.08 (4.60–6.73) | 0.493 | ΔM: 0.08 (–0.16, 0.34) |
| TC, median (IQR), mmol/L | 4.31 (3.61–5.04) | 4.36 (3.70–5.11) | 4.12 (3.54–4.91) | 0.107 | ΔM: –0.19 (–0.42, 0.04) |
| Triglyceride, median (IQR), mmol/L | 1.14 (0.86–1.57) | 1.18 (0.88–1.60) | 1.07 (0.80–1.54) | 0.127 | ΔM: –0.10 (–0.22, 0.03) |
| HDL-C, median (IQR), mmol/L | 1.10 (0.94–1.31) | 1.14 (0.98–1.37) | 1.03 (0.89–1.25) | 0.001* | ΔM: –0.11 (–0.17, –0.05) |
| LDL-C, median (IQR), mmol/L | 2.73 (2.21–3.32) | 2.78 (2.25–3.35) | 2.69 (2.17–3.31) | 0.391 | ΔM: –0.07 (–0.25, 0.10) |
| ApoA1, median (IQR), g/L | 1.27 (1.10–1.41) | 1.29 (1.17–1.47) | 1.20 (1.02–1.36) | < 0.001* | ΔM: –0.12 (–0.18, –0.06) |
| ApoB, median (IQR), g/L | 0.73 (0.63–0.88) | 0.71 (0.63–0.89) | 0.76 (0.63–0.88) | 0.652 | ΔM: 0.01 (–0.03, 0.06) |
| Lipoprotein (a), median (IQR), mg/L | 191.8 (101.8–332.7) | 182.9 (88.5–313.0) | 204.1 (108.4–356.6) | 0.066 | ΔM: 29.40 (–2.20, 65.60) |
| Uric acid, median (IQR), umol/L | 285.5 (240.3–338.1) | 288.3 (242.3–341.6) | 280.8 (235.5–331.7) | 0.450 | ΔM: –7.20 (–24.80, 11.10) |
| Fibrinogen, median (IQR), g/L | 2.80 (2.41–3.31) | 2.75 (2.34–3.19) | 2.86 (2.55–3.55) | 0.020* | ΔM: 0.18 (0.03, 0.34) |
| Serum homocysteine, median (IQR), umol/L | 13.2 (10.6–16.5) | 12.6 (9.6–15.4) | 14.1 (11.4–19.0) | 0.003* | ΔM: 1.70 (0.60, 2.80) |
| SOD, median (IQR), U/mL | 141.7 (127.5–154.0) | 148.1 (140.3–158.9) | 129.1 (119.7–138.6) | < 0.001* | ΔM: –20.10 (–23.70, –16.30) |
| Severe CSVD burden, n (%) | 130 (45.5) | 36 (22.5) | 94 (74.6) | < 0.001* | φ:0.52 (0.42, 0.62) |
Characteristics of participants between normal cognitive group and VaMCI group.
*Denotes significance at a P-value of < 0.05. Effect sizes: ΔM = Hodges-Lehmann median difference (VaMCI group minus normal group) for continuous variables; φ = Phi coefficient for categorical variables. Values in parentheses represent 95% confidence intervals. VaMCI, vascular mild cognitive impairment; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B; SOD, superoxide dismutase; CSVD, cerebral small vessel disease.
Superoxide dismutase levels and vascular mild cognitive impairment
In unadjusted logistic regression analysis, elevated SOD levels were associated with a decreased likelihood of VaMCI (OR: 0.926; 95% CI: 0.908–0.945; P < 0.001). This association remained statistically significant in multivariate logistic regression analysis (OR: 0.919; 95% CI: 0.896–0.943; P < 0.001), after adjusting for potential confounders (Table 2). SOD levels exhibited a negative relationship with the risk of VaMCI. Compared to the lowest quartile of SOD levels, individuals in the highest quartile demonstrated a significantly reduced risk of VaMCI (OR: 0.039; 95% CI: 0.016–0.100; P for trend < 0.001) after controlling for age, education, hypertension, and serum homocysteine (Table 3).
TABLE 2
| Univariable analysis | Multivariable analysis | |||
|---|---|---|---|---|
| Variable | OR (95% CI) | P-value | OR (95% CI) | P-value |
| Age | 1.093 (1.048–1.141) | <0.001 | 1.077 (1.013–1.146) | 0.018 |
| Education | 0.944 (0.893–0.998) | 0.042 | 0.921 (0.849–0.999) | 0.046 |
| HDL-C | 0.223 (0.088–0.561) | 0.001 | 0.962 (0.139–6.639) | 0.969 |
| ApoA1 | 0.112 (0.040–0.318) | <0.001 | 0.132 (0.015–1.159) | 0.068 |
| Lipoprotein (a) | 1.001 (1.000–1.002) | 0.068 | 1.001 (0.999–1.003) | 0.286 |
| Fibrinogen | 1.349 (1.020–1.784) | 0.036 | 1.032 (0.706–1.508) | 0.872 |
| Serum homocysteine | 1.038 (1.008–1.070) | 0.014 | 1.019 (0.978–1.062) | 0.361 |
| SOD | 0.926 (0.908–0.945) | <0.001 | 0.919 (0.896–0.943) | <0.001 |
| Severe CSVD burden | 10.12 (5.86–17.48) | <0.001 | 13.48 (6.34–28.68) | <0.001 |
The logistic regression analyses between possible predictors and VaMCI in elderly patients.
VaMCI, vascular mild cognitive impairment; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; ApoA1, apolipoprotein A1; SOD, superoxide dismutase; CSVD, cerebral small vessel disease.
TABLE 3
| SOD levels, median (range) | |||||
|---|---|---|---|---|---|
| Model | Quartile 1 119.8 (≤ 127.5) |
Quartile 2 134.5 (127.6–141.6) |
Quartile 3 146.3 (141.7–154.0) |
Quartile 4 164.4 (≥ 154.1) |
P-value for trend |
| VaMCI | |||||
| Model 1 | 1.00 (reference) | 0.244 (0.112–0.530) | 0.053 (0.023–0.122) | 0.041 (0.017–0.098) | <0.001 |
| Model 2 | 1.00 (reference) | 0.241 (0.108–0.536) | 0.053 (0.022–0.127) | 0.042 (0.017–0.104) | <0.001 |
| Model 3 | 1.00 (reference) | 0.262 (0.117–0.588) | 0.058 (0.024–0.139) | 0.039 (0.016–0.100) | <0.001 |
| Effect size (Q4 vs. Q1) | h = 1.453 (1.125–1.781) | ||||
| Severe CSVD burden | |||||
| Model 1 | 1.00 (reference) | 0.823 (0.426–1.591) | 0.454 (0.233–0.884) | 0.411 (0.209–0.807) | 0.003 |
| Model 2 | 1.00 (reference) | 0.871 (0.445–1.707) | 0.505 (0.255–0.998) | 0.486 (0.243–0.971) | 0.018 |
| Model 3 | 1.00 (reference) | 0.954 (0.476–1.914) | 0.547 (0.027–1.106) | 0.442 (0.214–0.913) | 0.011 |
| Effect size (Q4 vs. Q1) | h = 0.440 (0.112–0.767) | ||||
ORs (and 95% CIs) of VaMCI and severe CSVD burden according to quartiles of SOD levels.
Model 1: Unadjusted; Model 2: Adjusted for age and education. Model 3: Adjusted for age, education, hypertension, and serum homocysteine. Cohen’s h effect sizes are reported for the comparison between the highest (Q4) and lowest (Q1) SOD quartiles, with 95% confidence intervals in parentheses.
Superoxide dismutase levels and severe CSVD burden
In unadjusted logistic regression analysis, elevated SOD levels were associated with a decreased likelihood of severe CSVD burden (OR: 0.980; 95% CI: 0.968–0.992; P = 0.001). This association remained statistically significant in multivariate logistic regression analysis (OR: 0.979; 95% CI:0.965–0.993; P = 0.003), after adjusting for potential confounders (Table 4). SOD levels exhibited a negative relationship with the risk of severe CSVD burden. Compared to the lowest quartile of SOD levels, individuals in the highest quartile demonstrated a significantly reduced risk of severe CSVD burden (OR: 0.442; 95% CI: 0.214–0.913; P for trend = 0.011) after controlling for age, education, hypertension, and serum homocysteine (Table 3).
TABLE 4
| Variable | Univariable analysis | Multivariable analysis | ||
|---|---|---|---|---|
| OR (95% CI) | P-value | OR (95% CI) | P-value | |
| Age | 1.078 (1.034–1.124) | <0.001 | 1.083 (1.031–1.137) | 0.001 |
| Sex (male) | 2.008 (1.247–3.233) | 0.004 | 1.545 (0.852–2.800) | 0.152 |
| Education | 0.981 (0.929–1.036) | 0.494 | – | – |
| BMI | 1.015 (0.949–1.085) | 0.667 | – | – |
| Current smoking | 1.735 (0.950–3.167) | 0.073 | 1.700 (0.805–3.588) | 0.164 |
| Current drinking | 0.929 (0.477–1.809) | 0.829 | – | – |
| Hypertension | 1.692 (1.014–2.824) | 0.044 | 2.036 (1.099–3.775) | 0.024 |
| SBP | 1.012 (1.000–1.024) | 0.055 | 1.010 (0.996–1.024) | 0.146 |
| DBP | 1.012 (0.993–1.032) | 0.218 | – | – |
| Diabetes | 1.701 (1.018–2.840) | 0.042 | 1.332 (0.653–2.717) | 0.430 |
| Coronary heart disease | 0.932 (0.524–1.658) | 0.810 | – | – |
| History of stroke | 1.670 (1.036–2.690) | 0.035 | 1.393 (0.815–2.383) | 0.226 |
| Fasting plasma glucose | 1.139 (1.003–1.293) | 0.044 | 1.143 (0.957–1.364) | 0.140 |
| TC | 0.865 (0.682–1.098) | 0.235 | – | – |
| Triglyceride | 1.042 (0.839–1.294) | 0.712 | – | – |
| HDL-C | 0.323 (0.132–0.789) | 0.013 | 0.309 (0.066–1.443) | 0.135 |
| LDL-C | 0.892 (0.651–1.223) | 0.478 | – | – |
| ApoA1 | 0.389 (0.151–1.001) | 0.050 | 1.727 (0.328–9.101) | 0.519 |
| ApoB | 1.063 (0.311–3.633) | 0.922 | – | – |
| Lipoprotein (a) | 1.000 (0.999–1.001) | 0.822 | – | – |
| Uric acid | 1.002 (0.999–1.004) | 0.254 | – | – |
| Fibrinogen | 1.144 (0.881–1.485) | 0.313 | – | – |
| Serum homocysteine | 1.051 (1.017–1.086) | 0.003 | 1.046 (1.010–1.084) | 0.012 |
| SOD | 0.980 (0.968–0.992) | 0.001 | 0.979 (0.965–0.993) | 0.003 |
The logistic regression analyses between possible predictors and severe CSVD burden in elderly patients.
CSVD, cerebral small vessel disease; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B; SOD, superoxide dismutase.
Mediation analysis
The optimal threshold for SOD levels in participants with VaMCI was determined to be 138.55, with an area under the curve (AUC) of 0.813 (Figure 1). Based on this threshold, participants were categorized into two groups: those with elevated SOD levels and those with lower SOD levels. Mediation analysis revealed a significant direct effect (c’ = –0.459, P < 0.001) and total effect (c = –0.563, P < 0.001) between elevated SOD levels and VaMCI. When severe CSVD burden was introduced as a mediator, the indirect effect (ab = –0.105, P = 0.001) became significant, accounting for 18.7% of the total effect (Figure 2A). After adjusting for potential confounders, the indirect effect of severe CSVD burden remained significant, contributing 14.8% to the total effect (Figure 2B).
FIGURE 1
ROC curve of SOD levels for VaMCI. The optimal threshold for SOD levels in participants with VaMCI was determined to be 138.55. The specificity was 0.787 (1–0.213) and sensitivity was 0.754. The area under the curve (AUC) was 0.813.
FIGURE 2
Mediation analysis is shown for the presence of severe CSVD burden as a mediator in the relation between SOD levels and VaMCI. (A) Unadjusted. (B) Adjusted for age, education, hypertension, and serum homocysteine.
Discussion
In this study, we conducted a retrospective analysis to investigate the relationship between SOD levels and both VaMCI and CSVD burden in elderly patients. Our findings indicate that elevated SOD levels serve as a protective factor against severe CSVD burden and VaMCI in elderly patients, even after adjusting for potential confounding variables. These negative associations were more pronounced in patients with higher SOD levels. Additionally, our results suggest that the effect of increased SOD levels on VaMCI is partly mediated by the presence of a severe CSVD burden, potentially indicating that higher SOD levels may mitigate severe CSVD burden, thereby possibly reducing the risk of VaMCI. This underscores the critical role of SOD in modulating the progression of these conditions.
Oxidative stress has been implicated in the pathogenesis of various neurological disorders, including VCI (Luca and Luca, 2019) and CSVD (Grochowski et al., 2018). In elderly populations, the progressive accumulation of oxidative damage may exacerbate the severity of these conditions, potentially contributing to cognitive decline and other neurological manifestations. SOD is a vital antioxidant enzyme that serves as a critical indicator of the capacity to scavenge oxygen free radicals, thereby playing an indispensable role in mitigating oxidative stress (Fukai and Ushio-Fukai, 2011; Fanoudi et al., 2019).
Oxidative stress, intricately associated with inflammation, has been identified as a critical determinant in the pathogenesis and progression of CSVD (De Silva and Miller, 2016). The accumulation of localized ROS, which originates during the early stages of brain vascular injury, can both precipitate and result from mitochondrial dysfunction, endothelial dysfunction, inflammatory responses, and cellular apoptosis (Grochowski et al., 2018). Oxidative stress progressively intensifies with advancing age and in the presence of multiple CSVD risk factors (Liguori et al., 2018), further underscoring its pivotal role in the pathology of CSVD. Several studies demonstrated a significant association between SOD and CSVD. Zhu et al. found that decreased SOD levels act as an independent risk factor for WMH (Zhu et al., 2019). Furthermore, research has indicated that specific antioxidants may alleviate CSVD or CSVD-related cognitive impairments through the enhancement of SOD levels (Jiang et al., 2021; Wang and Hu, 2018). However, to the best of our knowledge, no prior studies have examined the relationship between SOD levels and the CSVD burden, which serves as a comprehensive indicator of CSVD severity (Duering et al., 2023). In our study, we observed that elevated SOD levels serve as a protective factor against severe CSVD burden in elderly patients. The negative correlation was more pronounced in patients with higher SOD levels, suggesting a potential dose-response relationship.
Interestingly, oxidative stress also has been demonstrated to be critically involved in the pathogenesis of VCI (Rundek et al., 2022; Luca and Luca, 2019). An increasing body of evidence suggests that low levels of SOD are closely associated with the onset and progression of cognitive impairment (Zhu et al., 2019; Fracassi et al., 2021). The underlying mechanisms are multifaceted and complex. Previous studies have shown that SOD deficiency accelerates amyloid β oligomerization or promotes tau protein phosphorylation in Alzheimer’s disease (AD) model mice (Murakami et al., 2011; Massaad et al., 2009; McLimans et al., 2019), suggesting a critical pathway by which SOD deficiency may influence VCI. More importantly, elevated levels of SOD may alleviate oxidative stress, suppress inflammatory responses, and safeguard the integrity of the blood-brain barrier (Fukai and Ushio-Fukai, 2011; Zhong et al., 2008; Ishihara et al., 2015), which are essential for reducing the risk of VCI. A study demonstrated that low SOD levels were associated with an increased risk of cognitive impairment after mild acute ischemic stroke, an important subtype of VCI (Zhang et al., 2022). Conversely, a community-based prospective study reported that higher SOD activity was linked to an elevated risk of cognitive decline in older Chinese adults (Sun et al., 2019). However, this study did not further differentiate between subtypes and degrees of cognitive impairment.
Importantly, previous studies investigating the relationship between SOD and cognitive impairment have not adequately considered the impact of small cerebral vascular disease, which exerts a significant influence on cognitive impairment. This omission may have resulted in an incomplete understanding of the underlying mechanisms contributing to cognitive impairment. Our study identified low SOD levels as an independent risk factor for VaMCI. Further analysis demonstrated that, after adjusting for potential confounding factors, part of the association between SOD levels and VaMCI was mediated by the presence of a severe CSVD burden.
The primary strength of our study lies in its consideration of CSVD when examining the relationship between SOD levels and VaMCI in elderly patients. However, several limitations must be acknowledged. Firstly, the retrospective nature of this study design precludes a definitive establishment of causality. While the research identified an association between SOD levels and both CSVD burden and VaMCI in elderly patients, the direction of causation remains unclear. It is plausible that changes in SOD levels are a consequence of CSVD and VaMCI progression rather than an initiating factor. Secondly, the retrospective design introduces potential selection bias. The patient cohort was likely drawn from specific hospitals or medical records, which may not be representative of the broader elderly population affected by CSVD and VaMCI. Thirdly, unaccounted confounding factors present a significant challenge. Numerous variables could influence the relationship between SOD, CSVD, and VaMCI, and accurately measuring and controlling for all these confounders in a retrospective study can be difficult. Future prospective studies are necessary to address these limitations.
Conclusion
In conclusion, our study provides evidence that elevated SOD levels are inversely associated with the risk of VaMCI or CSVD burden in elderly patients. Notably, severe CSVD burden significantly moderates the relationship between higher SOD levels and VaMCI. These findings suggest that a portion of the protective effect of elevated SOD levels on VaMCI may be attenuated by the exacerbation of CSVD burden. Elevated SOD levels represent a promising therapeutic target for protecting elderly patients from VaMCI and CSVD. Further research is warranted to explore potential strategies aimed at enhancing SOD activity, which could ultimately contribute to improved neurological health and cognitive function in older adults.
Statements
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
The study was approved by the Ethics Committee of Hebei General Hospital. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
Author contributions
XX: Formal analysis, Writing – original draft, Project administration, Conceptualization, Methodology, Data curation, Investigation. ML: Writing – review & editing, Project administration, Data curation, Methodology, Investigation, Supervision. TG: Writing – review & editing, Software. ZT: Software, Methodology, Writing – review & editing. JF: Writing – review & editing, Methodology. JX: Investigation, Writing – review & editing. QQ: Data curation, Writing – review & editing. YX: Investigation, Methodology, Writing – review & editing. PL: Writing – review & editing, Conceptualization. YD: Writing – review & editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This work was supported by The Scientific and Technological Innovation 2030-Major Project Subject of “Brain Science and Brain-inspired Research” (Grant no. 2021ZD0201807).
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2025.1720400/full#supplementary-material
References
1
Balendra V. Singh S. K. (2021). Therapeutic potential of astaxanthin and superoxide dismutase in Alzheimer’s disease.Open Biol.11:210013. 10.1098/rsob.210013
2
De Silva T. M. Miller A. A. (2016). Cerebral small vessel disease: Targeting oxidative stress as a novel therapeutic strategy?Front. Pharmacol.7:61. 10.3389/fphar.2016.00061
3
Duering M. Biessels G. J. Brodtmann A. Chen C. Cordonnier C. de Leeuw F. E. et al (2023). Neuroimaging standards for research into small vessel disease-advances since 2013.Lancet Neurol.22602–618. 10.1016/S1474-4422(23)00131-X
4
Fanoudi S. Alavi M. S. Hosseini M. Sadeghnia H. R. (2019). Nigella sativa and thymoquinone attenuate oxidative stress and cognitive impairment following cerebral hypoperfusion in rats.Metab. Brain Dis.341001–1010. 10.1007/s11011-019-00394-4
5
Fazekas F. Kleinert R. Offenbacher H. Schmidt R. Kleinert G. Payer F. et al (1993). Pathologic correlates of incidental MRI white matter signal hyperintensities.Neurology431683–1689. 10.1212/wnl.43.9.1683
6
Fracassi A. Marcatti M. Zolochevska O. Tabor N. Woltjer R. Moreno S. et al (2021). Oxidative damage and antioxidant response in frontal cortex of demented and nondemented individuals with Alzheimer’s neuropathology.J. Neurosci.41538–554. 10.1523/JNEUROSCI.0295-20.2020
7
Fukai T. Ushio-Fukai M. (2011). Superoxide dismutases: Role in redox signaling, vascular function, and diseases.Antioxid. Redox Signal.151583–1606. 10.1089/ars.2011.3999
8
Gregoire S. M. Chaudhary U. J. Brown M. M. Yousry T. A. Kallis C. Jager H. R. et al (2009). The Microbleed Anatomical Rating Scale (MARS): Reliability of a tool to map brain microbleeds.Neurology731759–1766. 10.1212/WNL.0b013e3181c34a7d
9
Grochowski C. Litak J. Kamieniak P. Maciejewski R. (2018). Oxidative stress in cerebral small vessel disease. Role of reactive species.Free Radic. Res.521–13. 10.1080/10715762.2017.1402304
10
Hannawi Y. (2024). Cerebral small vessel disease: A review of the pathophysiological mechanisms.Transl. Stroke Res.151050–1069. 10.1007/s12975-023-01195-9
11
He Y. He T. Li H. Chen W. Zhong B. Wu Y. et al (2024). Deciphering mitochondrial dysfunction: Pathophysiological mechanisms in vascular cognitive impairment.Biomed. Pharmacother.174:116428. 10.1016/j.biopha.2024.116428
12
Huijts M. Duits A. van Oostenbrugge R. J. Kroon A. A. de Leeuw P. W. Staals J. (2013). Accumulation of MRI markers of cerebral small vessel disease is associated with decreased cognitive function. A study in first-ever lacunar stroke and hypertensive patients.Front. Aging Neurosci.5:72. 10.3389/fnagi.2013.00072
13
Iadecola C. Duering M. Hachinski V. Joutel A. Pendlebury S. T. Schneider J. A. et al (2019). Vascular cognitive impairment and dementia: JACC Scientific Expert Panel.J. Am. Coll. Cardiol.733326–3344. 10.1016/j.jacc.2019.04.034
14
Inoue Y. Shue F. Bu G. Kanekiyo T. (2023). Pathophysiology and probable etiology of cerebral small vessel disease in vascular dementia and Alzheimer’s disease.Mol. Neurodegener.18:46. 10.1186/s13024-023-00640-5
15
Ishihara Y. Takemoto T. Itoh K. Ishida A. Yamazaki T. (2015). Dual role of superoxide dismutase 2 induced in activated microglia: Oxidative stress tolerance and convergence of inflammatory responses.J. Biol. Chem.29022805–22817. 10.1074/jbc.M115.659151
16
Islam M. N. Rauf A. Fahad F. I. Emran T. B. Mitra S. Olatunde A. et al (2022). Superoxide dismutase: An updated review on its health benefits and industrial applications.Crit. Rev. Food Sci. Nutr.627282–7300. 10.1080/10408398.2021.1913400
17
Jiang S. Ma X. Chen Y. Gu B. Sun N. Xiao H. (2021). Effects of ginkgo diterpene lactone on brain inflammation and oxidative stress in rats with cognitive impairment of cerebral small vessel disease.Am. J. Transl. Res.136382–6390.
18
Kim J. M. Park K. Y. Kim H. R. Ahn H. Y. Pantoni L. Park M. S. et al (2021). Association of bone mineral density to cerebral small vessel disease burden.Neurology96e1290–e1300. 10.1212/WNL.0000000000011526
19
Liguori I. Russo G. Curcio F. Bulli G. Aran L. Della-Morte D. et al (2018). Oxidative stress, aging, and diseases.Clin. Interv. Aging13757–772. 10.2147/CIA.S158513
20
Lissek V. Suchan B. (2021). Preventing dementia? Interventional approaches in mild cognitive impairment.Neurosci. Biobehav. Rev.122143–164. 10.1016/j.neubiorev.2020.12.022
21
Lu J. Li D. Li F. Zhou A. Wang F. Zuo X. et al (2011). Montreal cognitive assessment in detecting cognitive impairment in Chinese elderly individuals: A population-based study.J. Geriatr. Psychiatry Neurol.24184–190. 10.1177/0891988711422528
22
Luca M. Luca A. (2019). Oxidative stress-related endothelial damage in vascular depression and vascular cognitive impairment: Beneficial effects of aerobic physical exercise.Oxid. Med. Cell Longev.2019:8067045. 10.1155/2019/8067045
23
Massaad C. A. Washington T. M. Pautler R. G. Klann E. (2009). Overexpression of SOD-2 reduces hippocampal superoxide and prevents memory deficits in a mouse model of Alzheimer’s disease.Proc. Natl. Acad. Sci. U. S. A.10613576–13581. 10.1073/pnas.0902714106
24
McLimans K. E. Clark B. E. Plagman A. Pappas C. Klinedinst B. Anatharam V. et al (2019). Is cerebrospinal fluid superoxide dismutase 1 a biomarker of tau but not amyloid-induced neurodegeneration in Alzheimer’s disease?Antioxid. Redox Signal.31572–578. 10.1089/ars.2019.7762
25
Mok V. Cai Y. Markus H. S. (2024). Vascular cognitive impairment and dementia: Mechanisms, treatment, and future directions.Int. J. Stroke19838–856. 10.1177/17474930241279888
26
Murakami K. Shimizu T. Irie K. (2011). Formation of the 42-mer Amyloid beta radical and the therapeutic role of superoxide dismutase in Alzheimer’s disease.J. Amino Acids2011:654207. 10.4061/2011/654207
27
Nolfi-Donegan D. Braganza A. Shiva S. (2020). Mitochondrial electron transport chain: Oxidative phosphorylation, oxidant production, and methods of measurement.Redox Biol.37:101674. 10.1016/j.redox.2020.101674
28
Quick S. Moss J. Rajani R. M. Williams A. (2021). A vessel for change: Endothelial dysfunction in cerebral small vessel disease.Trends Neurosci.44289–305. 10.1016/j.tins.2020.11.003
29
Rundek T. Tolea M. Ariko T. Fagerli E. A. Camargo C. J. (2022). Vascular Cognitive Impairment (VCI).Neurotherapeutics1968–88. 10.1007/s13311-021-01170-y
30
Skrobot O. A. Black S. E. Chen C. DeCarli C. Erkinjuntti T. Ford G. A. et al (2018). Progress toward standardized diagnosis of vascular cognitive impairment: Guidelines from the vascular impairment of cognition classification consensus study.Alzheimers Dement.14280–292. 10.1016/j.jalz.2017.09.007
31
Staals J. Booth T. Morris Z. Bastin M. E. Gow A. J. Corley J. et al (2015). Total MRI load of cerebral small vessel disease and cognitive ability in older people.Neurobiol. Aging362806–2811. 10.1016/j.neurobiolaging.2015.06.024
32
Sun D. Sun X. Xu Y. Wu T. Tao L. (2019). Superoxide dismutase activity and risk of cognitive decline in older adults: Findings from the Chinese longitudinal healthy longevity survey.Exp. Gerontol.11872–77. 10.1016/j.exger.2019.01.010
33
Teng Z. Feng J. Dong Y. Xu J. Jiang X. Chen H. et al (2022). Triglyceride glucose index is associated with cerebral small vessel disease burden and cognitive impairment in elderly patients with type 2 diabetes mellitus.Front. Endocrinol.13:970122. 10.3389/fendo.2022.970122
34
van der Flier W. M. Skoog I. Schneider J. A. Pantoni L. Mok V. Chen C. et al (2018). Vascular cognitive impairment.Nat. Rev. Dis. Primers4:18003. 10.1038/nrdp.2018.3
35
Wang W. Hu W. (2018). Salvianolic acid B recovers cognitive deficits and angiogenesis in a cerebral small vessel disease rat model via the STAT3/VEGF signaling pathway.Mol. Med. Rep.173146–3151. 10.3892/mmr.2017.8203
36
Wardlaw J. M. Smith E. E. Biessels G. J. Cordonnier C. Fazekas F. Frayne R. et al (2013). Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration.Lancet Neurol.12822–838. 10.1016/S1474-4422(13)70124-8
37
Zhang M. S. Liang J. H. Yang M. J. Ren Y. R. Cheng D. H. Wu Q. H. et al (2022). Low serum superoxide dismutase is associated with a high risk of cognitive impairment after mild acute ischemic stroke.Front. Aging Neurosci.14:834114. 10.3389/fnagi.2022.834114
38
Zhong Z. Deane R. Ali Z. Parisi M. Shapovalov Y. O’Banion M. K. et al (2008). ALS-causing SOD1 mutants generate vascular changes prior to motor neuron degeneration.Nat. Neurosci.11420–422. 10.1038/nn2073
39
Zhu S. Wei X. Yang X. Huang Z. Chang Z. Xie F. et al (2019). Plasma lipoprotein-associated phospholipase A2 and superoxide dismutase are independent predicators of cognitive impairment in cerebral small vessel disease patients: Diagnosis and assessment.Aging Dis.10834–846. 10.14336/AD.2019.0304
Summary
Keywords
cerebral small vessel disease, mild cognitive impairment, oxidative stress, superoxide dismutase, vascular cognitive impairment
Citation
Xie X, Li M, Guan T, Teng Z, Feng J, Xu J, Qi Q, Xiao Y, Lv P and Dong Y (2026) Superoxide dismutase is associated with cerebral small vessel disease burden and vascular mild cognitive impairment in elderly patients. Front. Neurosci. 19:1720400. doi: 10.3389/fnins.2025.1720400
Received
21 October 2025
Revised
07 December 2025
Accepted
09 December 2025
Published
05 January 2026
Volume
19 - 2025
Edited by
Anuska V. Andjelkovic, University of Michigan, United States
Reviewed by
Cristiano Capurso, University of Foggia, Italy
Uygur Tanriverdi, Istanbul University-Cerrahpasa, Türkiye
Updates
Copyright
© 2026 Xie, Li, Guan, Teng, Feng, Xu, Qi, Xiao, Lv and Dong.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yanhong Dong, 90030159@hebmu.edu.cn
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.