Back to Journals » Neuropsychiatric Disease and Treatment » Volume 19

Systemic Immune-Inflammation Index is Associated with Cerebral Small Vessel Disease Burden and Cognitive Impairment

Authors Xiao Y, Teng Z, Xu J, Qi Q, Guan T, Jiang X, Chen H, Xie X, Dong Y, Lv P 

Received 20 December 2022

Accepted for publication 15 February 2023

Published 21 February 2023 Volume 2023:19 Pages 403—413

DOI https://doi.org/10.2147/NDT.S401098

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Taro Kishi



Yining Xiao,1– 3 Zhenjie Teng,1– 3 Jing Xu,2,3 Qianqian Qi,2 Tianyuan Guan,1,2 Xin Jiang,2,3 Huifang Chen,2 Xiaohua Xie,2 Yanhong Dong,2,3 Peiyuan Lv1– 3

1Department of Neurology, Hebei Medical University, Shijiazhuang, Hebei, People’s Republic of China; 2Department of Neurology, Hebei General Hospital, Shijiazhuang, Hebei, People’s Republic of China; 3Hebei Provincial Key Laboratory of Cerebral Networks and Cognitive Disorders, Shijiazhuang, Hebei, People’s Republic of China

Correspondence: Peiyuan Lv, Department of Neurology, Hebei General Hospital, Shijiazhuang, Hebei, 050051, People’s Republic of China, Email [email protected]

Objective: This study sought to explore the associations of the systemic immune-inflammation index (SII) with total cerebral small vessel disease (CSVD) burden and cognitive impairment.
Methods: We enrolled 201 patients in the retrospective study with complete clinical and laboratory data. The SII was calculated as platelet count × neutrophil count/lymphocyte count. Cognitive function was evaluated by the Mini-Mental State Examination (MMSE). Total CSVD burden was assessed based on magnetic resonance imaging. We performed logistic regression models, Spearman correlation, and mediation analysis to evaluate the associations of SII with CSVD burden and cognitive impairment.
Results: After adjustment for confounding factors in the multivariate binary logistic regression model, elevated SII (odds ratio [OR], 3.263; 95% confidence interval [CI], 1.577– 6.752; P = 0.001) or severe CSVD burden (OR, 2.794; 95% CI, 1.342– 5.817; P = 0.006) was significantly associated with the risk of cognitive impairment. Correlation analyses revealed that SII levels were negatively associated with MMSE scores (rs = − 0.391, P < 0.001), and positively associated with the total CSVD burden score (rs = 0.361, P < 0.001). Moreover, SII was significantly related to the severity of the CSVD burden (OR, 2.674; 95% CI, 1.359– 5.263; P = 0.004). The multivariable-adjusted odds ratios (95% CI) in highest tertile versus lowest tertile of SII were 8.947 (3.315– 24.145) for cognitive impairment and 4.945 (2.063– 11.854) for severe CSVD burden, respectively. The effect of higher SII on cognitive impairment development was partly mediated by severe CSVD burden.
Conclusion: Elevated SII is associated with severe CSVD burden and cognitive impairment. The mediating role of severe CSVD burden suggests that higher SII may contribute to cognitive impairment through aggravating CSVD burden.

Keywords: systemic immune-inflammation index, inflammation, cognitive impairment, cerebral small vessel disease, total burden

Introduction

As life expectancies and the aging population across the world continue to rise, cognitive impairment is a major obstacle to healthy aging.1,2 Cerebral small vessel disease (CSVD), common in the elderly, has become a primary vascular contributor to cognitive impairment.3 It contributes to 45% of all dementias, posing a tremendous socioeconomic burden.4,5

CSVD is a chronic disease caused by various etiologies affecting the small intracerebral arteries and their distal branches, arterioles, capillaries, and small veins.6 White matter hyperintensity (WMH) and lacunes of presumed vascular origin, cerebral microbleeds (CMBs), and enlarged perivascular space (EPVS) are typical magnetic resonance imaging (MRI) features of the disease.7 Based on these MRI markers, the total CSVD burden score can serve as a better indicator of the severity of CSVD.8 It has been demonstrated that total CSVD burden is strongly associated with the development of cognitive impairment.9 Although many studies have focused on the etiological mechanisms of CSVD, they are not yet clear. Recent studies have found that inflammation is strongly associated with CSVD.10

Recently, the systemic immune-inflammation index (SII) has been proposed as a relatively new systemic inflammatory biomarker. SII is calculated using neutrophil, lymphocyte, and platelet counts in the peripheral blood, more comprehensively reflecting the systemic immune response and inflammatory status.11,12 Previous studies have revealed that SII has a very high prognostic value in various malignant tumors13,14 and are associated with poor outcomes in cerebrovascular,15 cardiovascular16 and autoimmune diseases.17 We were particularly interested in its positive correlations with basal ganglia EPVS (BG-EPVS) and modified WMH burden.18 In addition, several population-based studies have confirmed the association between SII and cognitive impairment.19–21 Therefore, we speculate that there may be some potential relationship among them. However, the association of SII with total CSVD burden as well as cognitive function has not been explored simultaneously.

In the current study, we explored whether a higher level of SII increases the severity of CSVD burden and the risk of cognitive impairment, as well as whether the effects of higher SII on cognitive impairment are mediated by the severe CSVD burden.

Materials and Methods

Study Population

This cross-sectional study retrospectively analyzed data from hospitalized patients at Hebei General Hospital between January 2018 and December 2021. We enrolled patients over 50 years of age who had the blood examinations, completed the cognitive function assessment, and underwent brain MRI to evaluate CSVD markers. Exclusion criteria: (1) patients with active infection or use of antibiotics within 2 weeks; (2) hematologic disorders, malignant tumors, autoimmune diseases; (3) recent immunosuppressant treatment; (4) with tumors of the brain or other systems, surgery, or severe trauma; (5) acute ischemic/hemorrhagic stroke, myocardial infarction; (6) white matter damage of non-vascular origin, such as metabolic encephalopathy, multiple sclerosis; (7) cognitive impairment may be caused by other conditions, such as carbon monoxide poisoning, hyperthyroidism, hypothyroidism, severe anxiety, or depression. Ultimately, a total of 201 eligible patients participated in the analyses. This study was conducted according to the declaration of Helsinki and approved by the Ethical Committees of Hebei General Hospital (No.2022166).

Clinical Characterization

All demographic and risk factors were acquired from medical records: age, sex, years of education, body mass index (BMI), smoking status, and alcohol consumption. Medical history was also collected, including hypertension, diabetes mellitus, coronary heart disease and stroke. Laboratory biomarker were measured, including total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), very low-density lipoprotein cholesterol (VLDL-C), total homocysteine (tHcy), uric acid (UA), neutrophil count, lymphocyte count, and platelet count. The SII was calculated as platelet count × neutrophil count/lymphocyte count.11

MRI Acquisition and Assessment

Brain MRI was performed in all eligible participants using 3.0-Tesla MRI scanners (Signa, GE Healthcare of American). The MRI protocol and detailed acquisition parameters were as follows: (1) T1-weighted imaging (T1WI), repetition time (TR)/echo time (TE) = 1909/20.2 milliseconds (ms), field of view (FOV) = 240 × 192mm2, acquisition matrix = 320 × 224, number of excitations (NEX) = 1; (2) T2-weighted imaging (T2WI), TR/TE = 5000/125 ms, FOV = 240 × 240mm2, acquisition matrix = 352 × 352, NEX = 1; (3) fluid-attenuated inversion recovery (FLAIR), TR/TE = 8502/159.4 ms, FOV = 240 × 240mm2, acquisition matrix = 256 × 256, NEX = 1; (4) susceptibility weighted imaging (SWI), TR/TE = 78.6/47.6 ms, FOV = 240 × 216mm2, acquisition matrix = 384 × 320, NEX = 1; (5) diffusion-weighted imaging (DWI), TR/TE = 4800/81.7 ms, FOV = 240 × 240mm2, acquisition matrix = 160 × 160, NEX = 1. Slice thickness was 2 mm in SWI, and 5 mm in T1WI, T2WI, FLAIR, and DWI.

The total CSVD burden score was calculated based on the neuroimaging markers of CSVD (WMH, lacunes, CMBs, and EPVS).8 Each component of CSVD was independently evaluated by two readers blinded to all participants’ data according to STRIVE criteria.7 Finally, a third reader assessed any images with inconsistent results. The presence and severity of periventricular and deep cerebral WMH ranged from 0 to 3 and were visually evaluated on T2WI and FLAIR images using the Fazekas scoring system.22 Lacune was defined as a subcortical lesion of between 3 mm and 15 mm in size with cerebrospinal fluid (CSF) intensity on T1WI and FLAIR.7 CMBs were small (less than 10 mm in diameter), homogeneous, round foci of hypointensity on SWI, distinct from vessel flow void. According to current consensus criteria and the Microbleed Anatomical Rating Scale,23 their presence and number were assessed. EPVS, a linear or round CSF-like signal space (generally <3 mm in diameter), is visible on T1WI and T2WI.7 BG-EPVS were rated using a validated 4-point visual rating scale: none, 0 point; 1–10 EPVS, 1 point; 11–20 EPVS, 2 point; 21–40 EPVS, 3 point; >40 EPVS, 4 point.24 The total CSVD burden scores were computed on an ordinal scale from 0 to 4, according to a point system designed by Wardlaw’s group. A point was allocated for each criterion: (1) WMH: pWMH Fazekas score = 3 or dWMH Fazekas score ≥2; (2) lacunes: one or more lacunes; (3) CMBs: one or more deep CMBs; (4) EPVS: the score ≥2 in BG-EPVS. When the score exceeded 2, we considered it a severe CSVD burden.25

Neurocognitive Assessment

A validated Chinese version of the Mini-Mental State Examination (MMSE) was administered to all eligible participants for assessment of cognitive function. Because MMSE performance was most strongly influenced by education, it is strongly recommended that education levels be taken into account when interpreting MMSE results. Therefore, education-stratified cut-off points were chosen in the current study according to a population-based normative in China: 17 for individuals without any education, 20 for those with 1–6 years of education, and 24 for those with more than 7 years of education.26

Statistical Analyses

Continuous variables with a normal distribution are presented as the mean ± standard deviation, and the 2-tailed Student’s t-test was performed for comparisons between two groups. While non-normally distributed data were presented as the median (the interquartile range), and the Mann–Whitney U-test was performed. Categorical variables were expressed as number (percentage), and compared between the two groups using the χ2 test. We applied binary logistic regression models to evaluate the associations of SII with cognitive function and the severity of CSVD burden. Trend tests in ORs across SII tertiles were performed with the median within each tertile as the variable. We conducted three logistic regression models. Model 1: unadjusted; Model 2: adjusted for age, sex, and years of education; Model 3: additionally adjusted for hypertension, history of stroke, HDL-C, and tHcy. To assess the relationship between SII and cognitive performance, Spearman correlations were calculated between SII levels and the MMSE score. Similarly, the association of SII with the total CSVD burden score was also performed with Spearman correlations. The predictive value of the SII level for cognitive impairment was identified by a receiver operating characteristic (ROC) curve. Determined by Youden Index, the optimal cut-off point for SII levels was identified. Statistical significance was defined as a P < 0.05. All data were analyzed using the SPSS 23.0 statistical software (IBM, Armonk, NY, USA).

Finally, we performed the mediation analysis using R, version 4.1.0 (R Foundation for Statistical Computing, Vienna, Austria). The bruceR and mediation packages for R to determine whether the severe CSVD burden mediates the relationship between higher SII and cognitive impairment. Mediation analysis was conducted using the simple mediation model (Model 4). A nonparametric bootstrap with 5000 samples was employed to estimate the impact more robustly.

Results

Participants Characteristics

A total of 201 patients (median age: 66 years, interquartile range: 59–73 years; 96 men, 105 women) were enrolled in the current analysis. Based on MMSE scores and years of education, the cognitive impairment group included 68 patients, and the no cognitive impairment group included 133 patients. In Table 1, the details of the two groups are presented. Compared to patients without cognitive impairment, those with cognitive impairment were more older (median age: 69.5 years, interquartile range: 64–75 years), with lower level of education, higher frequencies of hypertension (P < 0.05). Patients with cognitive impairment also had lower HDL-C, higher tHcy, SII and total CSVD burden score (P < 0.05).

Table 1 Characteristics of the Participants Between Cognitive Impairment Group and No Cognitive Impairment Group

Association of SII with Cognitive Impairment

In the current study, we performed logistic regression models to explore the relationship between SII and cognitive impairment (Table 2). Unadjusted binary logistic regression results showed that higher level of SII was associated with cognitive impairment (odds ratio [OR]: 4.466; 95% confidence interval [CI]: 2.350 to 8.498; P < 0.001). After adjusting for age, education, hypertension, HDL-C, tHcy, and severe CSVD burden, the multivariate binary logistic regression results demonstrated that SII was an independent risk factor for cognitive impairment (OR: 3.263; 95% CI: 1.577 to 6.752; P = 0.001).

Table 2 The Logistic Regression Analyses of Risk Factors for Cognitive Impairment

Based on SII tertiles, the eligible patients were divided into 3 groups (tertile 1: <414.06, tertile 2: 414.06–571.37, tertile 3: >571.37). Table 3 shows a significant trend between tertiles of SII levels and the risk of cognitive impairment. Higher levels of SII were significantly associated with cognitive impairment (OR for tertile 2: 4.572, 95% CI: 1.740 to 12.015, P for trend <0.001; OR for tertile 3: 10.829, 95% CI: 4.119 to 28.473, P for trend <0.001) after adjustment for age, sex, and education. The association remained significant but strengthened in tertile 2 and weakened in tertile 3 (OR for tertile 2: 4.493, 95% CI: 1.671 to 12.083, P for trend <0.001; OR for tertile 3: 8.947, 95% CI: 3.315 to 24.145, P for trend <0.001) after further adjustment for hypertension, history of stroke, HDL-C, and tHcy. Furthermore, we explored the correlation of SII levels and MMSE scores using correlation analysis (Figure 1). SII level was negatively associated with MMSE score (rs = −0.391, P < 0.001).

Table 3 ORs (and 95% CIs) of Cognitive Impairment According to Tertiles of SII

Figure 1 Correlation between SII and cognitive function. Scatterplot of SII and MMSE score (rs = −0.391, P < 0.001).

Association of SII with Severe CSVD Burden

We next performed logistic regression models to explore the relationship between SII and the severity of CSVD burden (Table 4). In univariable analyses, higher SII displayed a significant relationship between severe CSVD burden (OR: 3.545; 95% CI: 1.908 to 6.587; P < 0.001). When we adjusted for age, sex, hypertension, history of stroke, and tHcy in the multivariable analyses, the relationship remains significant (OR: 2.674; 95% CI: 1.359 to 5.263; P = 0.004).

Table 4 The Logistic Regression Analyses of Risk Factors for Severe CSVD Burden

Similarly, we explored the trend between tertiles of SII levels and severe CSVD burden (Table 5). The results showed that the highest level of SII was strongly associated with severe CSVD burden compared with the lowest (OR: 7.228, 95% CI: 3.265 to 15.999, P for trend <0.001). No significant association was observed between the tertile 1 and tertile 2 groups. The association remained significant but weakened in tertile 3 (OR: 6.235, 95% CI: 2.691 to 14.443, P for trend <0.001) after adjustment for age, sex, and education. After further adjustment for hypertension, history of stroke, HDL-C, and tHcy, the association also remained significant but weakened in tertile 3 (OR: 4.945, 95% CI: 2.063 to 11.854, P for trend <0.001), with no significant association between the tertile 1 and tertile 2 groups. In addition, there was a positive association between SII levels and total CSVD burden scores (rs = 0.361, P < 0.001) as shown in Table 6.

Table 5 ORs (and 95% CIs) of Severe CSVD Burden Score According to Tertiles of SII

Table 6 Spearman Correlation Analysis of SII and Total CSVD Burden Score

Mediation by Severe CSVD Burden

Figure 2 shows that rates of severe CSVD burden and cognitive impairment increase as SII levels increase. We performed mediation models to explore whether severe CSVD burden was a mediator for higher SII and cognitive impairment. First of all, the SII levels of patients with cognitive impairment were optimally cut-off at 434.49 (Figure 3), with an area under the curve (AUC) of 0.766 (95% CI: 0.698 to 0.834, P < 0.001). In accordance with the optimal cut-off point, we divided the cohort into two groups: those with higher and lower SII levels. Mediation analysis showed a significant total effect (c) and direct effect (c’) between higher SII and cognitive impairment (all P < 0.001), with a significant indirect effect (ab) when the severe CSVD burden score was included in the model (P = 0.044). And severe CSVD burden mediated 12.1% of the total effect after adjusting for age, hypertension, and tHcy (Figure 4). These results demonstrated that the presence of severe CSVD burden partly mediated the effect of higher SII levels on cognitive impairment.

Figure 2 Increasing rates of severe CSVD burden and cognitive impairment with increase in SII.

Figure 3 Receiver operating characteristic (ROC) curve of SII levels for cognitive impairment. The specificity was 0.526 (1–0.474) and sensitivity was 0.882.

Figure 4 Mediation analysis is shown for the presence of severe CSVD burden as a mediator in the relation between higher SII and cognitive impairment.

Discussion

In this retrospective study, we evaluated the associations of SII levels with cognitive impairment and CSVD burden. Our study revealed the following findings: First, patients with cognitive impairment had higher SII levels than those without cognitive impairment. Elevated SII was found to be significantly associated with the risk of cognitive impairment and severe CSVD burden. With a higher SII level, these associations were more prominent. Second, there was a significant negative correlation between SII and MMSE scores and a positive correlation with total CSVD burden scores. Third, the effect of higher SII on the development of cognitive impairment was partly mediated by severe CSVD burden, supporting the hypothesis that increased SII may aggravate CSVD, which in turn increases cognitive impairment risk.

Existing studies have investigated the relationship between SII and cognitive function in different pathological states. In a retrospective study, higher SII levels were associated with lower cognitive performance in breast cancer survivors who had ceased chemotherapy 20 years ago.21 There was also evidence that a higher level of SII was found to be an independent risk factor for cognitive decline postoperatively.20 Another retrospective study found that the elderly with a higher level of SII were at higher risk of developing mild cognitive impairment.19 A significant inverse association between SII and cognitive function was also found in the present study, and the association was stronger in the highest tertile compared with the lowest tertile, suggesting increased SII levels may accelerate cognitive impairment progression. However, the underlying mechanisms for the association remain unclear. Increasing evidence from studies in systemic inflammation and cognitive impairment may explain the association between higher SII and cognitive impairment, such as endothelial dysfunction, BBB disruption, vagal nerve stimulation, and oxidative stress.6,27

Growing evidence is showing that elevated SII is linked to the development of different imaging features of CSVD, which is important for the increased risk of cognitive impairment. It has been reported that higher SII levels were associated with greater WMH volume in a health check-up population.28 A prospective cohort study in a community population suggested that elevated SII was positively associated with moderate-to-severe BG-EPVS and modified WMH burden, with no significant differences between SII and the total CSVD burden score.18 Different from the previous study, we found the total CSVD burden score had a significant negative correlation with SII level, possibly due to older hospitalized patients, an increase in CSVD lesions and vascular risk factors. In addition, we extended previous research focusing on the CSVD burden severity. We found that SII was an independent risk factor for severe CSVD burden. However, only patients with the highest level of SII were significantly statistically associated with severe CSVD burden, suggesting elevated SII may increase the severity of CSVD. There were several possible explanations for the relationship between SII and CSVD. Neutrophils, lymphocytes, and platelets play vital roles in immune and inflammatory responses.29,30 Alterations in those blood cells were associated with immune-regulatory dysfunction.31 A sustained inflammatory stimulus induces endothelial dysfunction, leading to BBB disruption. On the one hand, the leakage of plasma constituents into the perivascular tissues might promote thickening and stiffening of arteriole walls, restricting vasodilation, and impairing further vasodilatation, oxygenation, and nutrient delivery.32 On the other hand, peripheral inflammatory cells migrate to the central nervous system (CNS) and microglia are activated.33,34 The chronic inflammatory microenvironment created in the CNS contributes to brain damage.

Interestingly, CSVD and cognitive impairment may share underlying mechanisms.3 Endothelial dysfunction, BBB disruption, and microglia activation also play critical roles in the development of cognitive impairment.35–37 It has been reported that higher CSVD burden was associated with cognitive impairment.9 The current study also observed that severe CSVD burden was positively related to a higher risk of cognitive impairment after adjusting for potential confounding factors. As SII levels increase, so do the rates of severe CSVD burden and cognitive impairment. Consequently, when investigating the relationship between SII and cognitive impairment, CSVD severity should be considered. This is also an important strength of our study. Our findings revealed a partial mediating role of severe CSVD burden on the association between SII and cognitive impairment, supporting the hypothesis that higher SII levels could aggravate the development of CSVD and then increase the risk of poor cognitive function.

Although we have some novel findings in the relationships among SII, CSVD, and cognitive impairment, there are several limitations that deserve consideration. First, due to the limitations of the retrospective study, no causal conclusions can be drawn from SII with severe CSVD burden and cognitive impairment. Although mediation analysis proved the causal hypothesis that severe CSVD burden partly mediated the association between higher SII levels and cognitive impairment, future prospective studies remain needed. Second, this is a small sample size study with only one center enrolling patients, which may contribute to selection bias. A longitudinal study with a larger sample size would be beneficial. Third, the average SII over time might be more appropriate for evaluating chronic inflammation. Fourth, although we adjusted for several potential confounding factors in our analyses, unmeasured covariates cannot be fully ruled out. Fifth, MMSE cannot adequately represent some intricacy of particular cognitive domains, more cognitive screening tool should be used. Continuous measures of CSVD biomarkers, such as volume of WMH, would present more strongly associated with cognition. Finally, dementia and mild cognitive impairment were not distinguished in the current study. These issues should require further follow-up studies to address them.

Conclusions

In summary, our study demonstrated that SII levels are associated with total CSVD burden severity and cognitive impairment. Results of mediation analysis revealed that higher SII increased the risk of cognitive impairment in part due to its influence on CSVD burden severity. However, the causality of this association needs to be further determined by prospective studies.

Ethics Statement

This study was reviewed and approved by Ethical Committee of Hebei General Hospital (No.2022166). The data of participants would be anonymized or kept confidential, and no rights or interests of participants would be violated. According to the national legislation and the institutional requirements, this study did not require written informed consent.

Acknowledgments

We thank the grants from 2022 Hebei Province Government-funded Excellent Talents Project in Clinical Medicine (Grant No. 2022-180-5) and Scientific and Technological Innovation 2030-Major Project Subject of “Brain Science and Brain-inspired Research” (Grant No. 2021ZD0201807).

Disclosure

The authors have no conflicts of interest to declare in this work.

References

1. Greenberg SM. Vascular contributions to brain health: cross-cutting themes. Stroke. 2022;53(2):391–393. doi:10.1161/STROKEAHA.121.034921

2. Jia L, Du Y, Chu L, et al. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. Lancet Public Health. 2020;5(12):e661–e671. doi:10.1016/S2468-2667(20)30185-7

3. Hamilton OKL, Backhouse EV, Janssen E, et al. Cognitive impairment in sporadic cerebral small vessel disease: a systematic review and meta-analysis. Alzheimers Dement. 2021;17(4):665–685. doi:10.1002/alz.12221

4. Bos D, Wolters FJ, Darweesh SKL, et al. Cerebral small vessel disease and the risk of dementia: a systematic review and meta-analysis of population-based evidence. Alzheimers Dement. 2018;14(11):1482–1492. doi:10.1016/j.jalz.2018.04.007

5. Zeestraten EA, Lawrence AJ, Lambert C, et al. Change in multimodal MRI markers predicts dementia risk in cerebral small vessel disease. Neurology. 2017;89(18):1869–1876. doi:10.1212/WNL.0000000000004594

6. Wardlaw JM, Smith C, Dichgans M. Mechanisms of sporadic cerebral small vessel disease: insights from neuroimaging. Lancet Neurol. 2013;12(5):483–497. doi:10.1016/S1474-4422(13)70060-7

7. Wardlaw JM, Smith EE, Biessels GJ, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013;12(8):822–838. doi:10.1016/S1474-4422(13)70124-8

8. Staals J, Makin SDJ, Doubal FN, Dennis MS, Wardlaw JM. Stroke subtype, vascular risk factors, and total MRI brain small-vessel disease burden. Neurology. 2014;83(14):1228–1234. doi:10.1212/WNL.0000000000000837

9. Li X, Yuan J, Qin W, et al. Higher total cerebral small vessel disease burden was associated with mild cognitive impairment and overall cognitive dysfunction: a propensity score-matched case-control study. Front Aging Neurosci. 2021;13:695732. doi:10.3389/fnagi.2021.695732

10. Low A, Mak E, Rowe JB, Markus HS, O’Brien JT. Inflammation and cerebral small vessel disease: a systematic review. Ageing Res Rev. 2019;53:100916. doi:10.1016/j.arr.2019.100916

11. Hu B, Yang XR, Xu Y, et al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin Cancer Res. 2014;20(23):6212–6222. doi:10.1158/1078-0432.CCR-14-0442

12. Ye Z, Hu T, Wang J, et al. Systemic immune-inflammation index as a potential biomarker of cardiovascular diseases: a systematic review and meta-analysis. Front Cardiovasc Med. 2022;9:933913. doi:10.3389/fcvm.2022.933913

13. Li J, Cao D, Huang Y, et al. The prognostic and clinicopathological significance of systemic immune-inflammation index in bladder cancer. Front Immunol. 2022;13:865643. doi:10.3389/fimmu.2022.865643

14. Fest J, Ruiter R, Mulder M, et al. The systemic immune-inflammation index is associated with an increased risk of incident cancer-A population-based cohort study. Int J Cancer. 2020;146(3):692–698. doi:10.1002/ijc.32303

15. Wang N, Yang Y, Qiu B, et al. Correlation of the systemic immune-inflammation index with short- and long-term prognosis after acute ischemic stroke. Aging. 2022;14(16):6567–6578. doi:10.18632/aging.204228

16. He L, Xie X, Xue J, Xie H, Zhang Y. Association of the systemic immune-inflammation index with all-cause mortality in patients with arteriosclerotic cardiovascular disease. Front Cardiovasc Med. 2022;9:952953. doi:10.3389/fcvm.2022.952953

17. Xie Y, Zhuang T, Ping Y, et al. Elevated systemic immune inflammation index level is associated with disease activity in ulcerative colitis patients. Clin Chim Acta. 2021;517:122–126. doi:10.1016/j.cca.2021.02.016

18. Jiang L, Cai X, Yao D, et al. Association of inflammatory markers with cerebral small vessel disease in community-based population. J Neuroinflammation. 2022;19(1):106. doi:10.1186/s12974-022-02468-0

19. Wang X, Li T, Li H, et al. Association of dietary inflammatory potential with blood inflammation: the prospective markers on mild cognitive impairment. Nutrients. 2022;14(12):2417. doi:10.3390/nu14122417

20. Lu W, Zhang K, Chang X, Yu X, Bian J. The association between systemic immune-inflammation index and postoperative cognitive decline in elderly patients. Clin Interv Aging. 2022;17:699–705. doi:10.2147/CIA.S357319

21. Van Der Willik KD, Koppelmans V, Hauptmann M, Compter A, Ikram MA, Schagen SB. Inflammation markers and cognitive performance in breast cancer survivors 20 years after completion of chemotherapy: a cohort study. Breast Cancer Res. 2018;20(1):135. doi:10.1186/s13058-018-1062-3

22. Fazekas F, Kleinert R, Offenbacher H, et al. Pathologic correlates of incidental MRI white matter signal hyperintensities. Neurology. 1993;43(9):1683–1689. doi:10.1212/wnl.43.9.1683

23. Gregoire SM, Chaudhary UJ, Brown MM, et al. The Microbleed Anatomical Rating Scale (Mars): reliability of a tool to map brain microbleeds. Neurology. 2009;73(21):1759–1766. doi:10.1212/WNL.0b013e3181c34a7d

24. Doubal FN, MacLullich AMJ, Ferguson KJ, Dennis MS, Wardlaw JM. Enlarged perivascular spaces on MRI are a feature of cerebral small vessel disease. Stroke. 2010;41(3):450–454. doi:10.1161/STROKEAHA.109.564914

25. Kim JM, Park KY, Kim HR, et al. Association of bone mineral density to cerebral small vessel disease burden. Neurology. 2021;96(9):1290–e1300. doi:10.1212/WNL.0000000000011526

26. Li H, Jia J, Yang Z, Moreau N. Mini-mental state examination in elderly Chinese: a population-based normative study. J Alzheimers Dis. 2016;53(2):487–496. doi:10.3233/JAD-160119

27. Conole ELS, Stevenson AJ, Maniega SM, et al. DNA methylation and protein markers of chronic inflammation and their associations with brain and cognitive aging. Neurology. 2021;97(23):e2340–e2352. doi:10.1212/WNL.0000000000012997

28. Nam KW, Kwon HM, Jeong HY, Park JH, Kwon H. Systemic immune-inflammation index is associated with white matter hyperintensity volume. Sci Rep. 2022;12(1):7379. doi:10.1038/s41598-022-11575-0

29. Adams NM, Grassmann S, Sun JC. Clonal expansion of innate and adaptive lymphocytes. Nat Rev Immunol. 2020;20(11):694–707. doi:10.1038/s41577-020-0307-4

30. Herrero-Cervera A, Soehnlein O, Kenne E. Neutrophils in chronic inflammatory diseases. Cell Mol Immunol. 2022;19(2):177–191. doi:10.1038/s41423-021-00832-3

31. Germolec DR, Shipkowski KA, Frawley RP, Evans E. Markers of inflammation. Methods Mol Biol. 2018;1803:57–79. doi:10.1007/978-1-4939-8549-4_5

32. Wardlaw JM, Smith C, Dichgans M. Small vessel disease: mechanisms and clinical implications. Lancet Neurol. 2019;18(7):684–696. doi:10.1016/S1474-4422(19)30079-1

33. Varatharaj A, Galea I. The blood-brain barrier in systemic inflammation. Brain Behav Immun. 2017;60:1–12. doi:10.1016/j.bbi.2016.03.010

34. Elwood E, Lim Z, Naveed H, Galea I. The effect of systemic inflammation on human brain barrier function. Brain Behav Immun. 2017;62:35–40. doi:10.1016/j.bbi.2016.10.020

35. Michels M, Vieira AS, Vuolo F, et al. The role of microglia activation in the development of sepsis-induced long-term cognitive impairment. Brain Behav Immun. 2015;43:54–59. doi:10.1016/j.bbi.2014.07.002

36. Hughes CG, Patel MB, Brummel NE, et al. Relationships between markers of neurologic and endothelial injury during critical illness and long-Term cognitive impairment and disability. Intens Care Med. 2018;44(3):345–355. doi:10.1007/s00134-018-5120-1

37. Rajeev V, Fann DY, Dinh QN, et al. Pathophysiology of blood brain barrier dysfunction during chronic cerebral hypoperfusion in vascular cognitive impairment. Theranostics. 2022;12(4):1639–1658. doi:10.7150/thno.68304

Creative Commons License © 2023 The Author(s). This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License. By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.