Association and predictive value analysis of β2-microglobulin and the severity of white matter hyperintensities

Association and predictive value analysis of β2-microglobulin and the severity of white matter hyperintensities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association and predictive value analysis of β2-microglobulin and the severity of white matter hyperintensities Wang Fei, Liu Tingting, He Jun, Xia Mingwu, Wang Rongfeng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6284537/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective : To investigate the association between serum β2-microglobulin (β2M) levels and the severity of White Matter Hyperintensities (WMHs) in patients with Cerebral Small Vessel Disease (CSVD), in addition to evaluate its predictive value for WMHs severity. Methods : We consecutively enrolled in patients with CSVD demonstrating WMHs on MRI from the Neurology Department at the Second People's Hospital of Hefei City between December 2021 and April 2024. Patient characteristics including demographic,baseline clinical, laboratory data, serum β2M levels and brain MRI features were collected. The severity of periventricular white matter hyperintensities (PWMHs) and deep white matter hyperintensities (DWMHs) was assessed using the Fazekas scale. Based on the sum of the scores from these two regions, patients were classified into a none or mild overall WMHs group (Fazekas score 0–2) and a moderate to severe overall WMHs group (Fazekas score 3–6). Patients were classified into a predominant periventricular white matter hyperintensities (pred-PWMHs) subgroup and a predominant deep white matter hyperintensities (pred-DWMHs) subgroup based on a score difference of ≥ 1 point between the two regions. Each subgroup was further divided into mild (Fazekas score 1) and moderate to severe groups (Fazekas score 2–3). Independent risk factors associated with moderate to severe overall WMHs, PWMHs and DWMHs in patients with CSVD were analyzed using univariate and multivariate logistic regression. The predictive value of β2M for moderate to severe overall WMHs was evaluated using receiver operating characteristic (ROC) curves. Results : A total of 346 patients were enrolled in the study, including 183 patients with none or mild overall WMHs and 163 patients with moderate to severe overall WMHs. Univariate analysis revealed that age ( P < 0.001), hypertension ( P < 0.001), fibrinogen ( P = 0.019), Hcyt( P = 0.005), CysC ( P = 0.001), Total cholesterol ( P = 0.025), LDL-C( P = 0.012), eGFR ( P < 0.001) and β2M ( P < 0.001) were associated with the severity of overall WMHs. Multivariate logistic regression analysis identified age ( OR :1.050, 95% CI :1.025–1.075, P < 0.001༉, hypertension ( OR :2.007, 95% CI :1.202–3.349, P = 0.008) and β2M ( OR :1.635, 95% CI :1.154–2.317, P = 0.006) as independent risk factors for moderate to severe overall WMHs. ROC curve analysis demonstrated that a β2M cut off value of 2.295 was significantly predictive of moderate to severe overall WMHs (AUC = 0.673, P < 0.001).In subgroup analysis, β2M was also identified as an independent risk factor for moderate to severe pred-PWMHs ( OR :3.134, 95% CI : 1.012–9.698, P = 0.048),while no association was observed with the severity of pred-DWMHs. Conclusion : Serum β2M levels are significantly associated with the severity of overall WMHs and pred-PWMHs,but not pred-DWMHs. Furthermore, β2M levels exhibit predictive value for moderate to severe overall WMHs. Cerebral Small Vessel Disease White Matter Hyperintensities β2-Microglobulin Magnetic Resonance Imaging Figures Figure 1 Figure 2 Figure 3 Introduction White Matter Hyperintensities (WMHs) are the most common subtype of Cerebral Small Vessel Disease (CSVD) in the elderly population. A community-based prevalence survey of people over 60 years of age found that WMHs were present in 65%-96% of the respondents [1] . WMHs are a well-established risk factor for both stroke and cognitive impairment, and a recent study demonstrated that common cerebrovascular disease risk factors, such as hypertension, indirectly mediated the development of stroke and cognitive impairment in 26.3% of patients through WMHs by mediation effect analysis [2] . The development of WMHs may involve multiple pathophysiological mechanisms, including impaired cerebral blood flow autoregulation, blood-brain barrier damage, dysfunction of small vessel endothelium, oxidative stress, and inflammation [3] . The search for potential biological markers behind different mechanisms may be important for the stratification of WMHs, early prevention and selection of interventions. CSVD and chronic kidney disease (CKD) are both chronic progressive diseases resulting from damage to the small blood vessels of parenchymal organs or their collateral blood and body fluid barriers, and they may share a similar pathophysiologic background. Decreased Estimated Glomerular Filtration Rate (eGFR) has been found to correlate with the severity of WMHs [4] . β2 -Microglobulin ( β2M ), a new endogenous renal biomarker, has been recently identified and is more sensitive than eGFR in the assessment of glomerular filtration function. It is more sensitive to the evaluation of glomerular filtration function than eGFR [5] . Elevated serum β2M is not only associated with an increased risk of cardiovascular events and all-cause mortality in dialysis patients with CKD [6] ,but also has been found to be associated with neurological diseases such as stroke [7] . Recent studies have demonstrated that elevated β2M is linked to atherosclerosis [6] , and serves as an independent predictor of ischemic stroke recurrence [8] . Whether β2M can be used as a reliable biological marker to reflect the severity of cerebral small-vessel pathology deserves to be further explored. A recent observational study confirmed the predictive value of β2M for the severity of WMHs, but did not assess eGFR levels or their potential impact on the predictive value of β2M [9] . In this study, we examined baseline blood β2M levels and eGFR and other related clinical data in patients with WMHs to investigate their correlation and predictive value with periventricular white matter high signals (PWMHs), deep white matter high signals (DWMHs), and the overall WMHs severity, respectively. Participants and methods Research object Consecutively, patients who were hospitalized in the Department of Neurology of the Second People's Hospital of Hefei City from December 2021 to April 2024 and were clearly qualified for the subtype of cerebral small vessel disease WMHs by MRI were collected. Inclusion criteria: a. The age of the enrolled patients was ≥40 years old; b. WMHs cranial MRI conformed to the manifestation of WMHs subtype in the CSVD imaging criteria (standards for reporting vascular changes on neuroimaging, STRIVE) [10] ; c. Patients with comorbid central nervous system infections, tumors, and hematologic disorders were excluded; and d. Patients with possible cerebral amyloid angiopathy, hereditary, inflammatory, and immune-mediated cerebral small-vessel disease were excluded. Material gathering Demographic information, cerebrovascular risk factors (including Smoking, Drinking, Hypertension, Diabetes, Stroke history, or transient ischemic attack), and body mass index were collected from all enrolled patients. On the second day of admission, routine venous blood samples were taken for biochemical analysis, including measurements of Coagulation parameters, Glycated hemoglobin, Creatinine, Urea nitrogen, Total cholesterol, Triglyceride, Low-density lipoprotein- cholesterol(LDL-C), High-density lipoprotein-cholesterol(HDL-C), Cystatin C(CysC), Homocysteine(Hcyt), Fibrinogen, and β2-microglobulin (β2M). estimated Glomerular Filtration Rate (eGFR) was calculated using the simplified MDRD formula; an eGFR <90 ml/(min·1.73 m²) indicated impaired glomerular filtration. The β2M level was measured by radioimmunoassay, with a reference range of 1.3–2.7 mg/L. Imaging Examination Brain MRI images were performed using a Siemens 1.5T magnetic resonance scanner (Model: Avanto I Class). The sequences included T1WI, T2WI, Fluid-Attenuated Inversion Recovery (FLAIR) and Diffusion-Weighted Imaging ( DWI ). Annet medical image management system was used to analyze the MRI imaging findings of patients, and the severity of WMHs in different parts was recorded. WMHs were defined as T2WI and FLAIR high-signal and T1WI equal or low-signal lesions in the periventricular or deep subcortical white matter regions of the lateral ventricles [10] . According to the Fazekas scale: paraventricular and deep white matter high signal was assessed, and the two sites were summed to obtain a total score (a total Fazekas score of 0-2 was categorized as none or mild WMHs, and 3-6 was categorized as moderate to severe WMHs) [11] . Given that periventricular and deep WMHs often occur simultaneously, in order to investigate the risk factors for PWMHs and DWMHs separately; in this study, with reference to the literature [12] , According to the difference between the periventricular and deep Fazekas scores ≥ 1, the patients were divided into the predominantly periventricular white matter high signal (pred-PWMHs ) subgroup and the predominantly deep white matter high signal (pred-DWMHs ) subgroup. Each subgroup was further divided into mild ( Fazekas score 1 ) and moderate to severe group ( Fazekas score 2-3 ) according to the Fazekas scale. MRI image data were blindly evaluated by two qualified physicians above the deputy director of neurology. If the results were inconsistent, they were consistent through consultation. Statistical analysis Statistical analyses were conducted by SPSS 25.0 software package (SPSS Inc., Chicago, IL, USA). quantitative information that conformed to normal distribution was expressed as mean ± standard deviation ( ±s), and quantitative information that did not conform to normal distribution was expressed as median and quartile (Q) of Q25 and Q75, and comparisons between groups were made Independent samples t-test or Mann-Whitney U rank-sum test was used; qualitative data were expressed as frequencies and percentages (%), and comparisons between groups were made using the chi-square test or Fisher's exact probability method. Variables with P < 0.1 in the univariate logistic regression analysis were included in the multivariate logistic regression model, and the odds ratio ( OR ) and 95 % confidence interval ( CI ) were calculated. Receiver operating characteristic curve ( ROC ) analysis was performed on β2M, and Area Under Curve ( AUC ) as well as the bounding value were calculated. P < 0.05 was considered statistically significant. Results In this study, 420 patients who initially met the inclusion criteria were identified. After screening to exclude individuals who did not meet the criteria, 346 patients were ultimately included in the final analysis ( Figure 1 ) . The mean age was 68.7±11.1 years, of which 170 were male and 47.1% had moderate to severe WMHs. A total of 99 cases of pred-PWMHs and 91 cases of pred-DWMHs were entered into the inter-subgroup comparison, of which the proportion of moderate-to-severe pred-PWMHs and pred-DWMHs were 44.4% and 64.8%, respectively. Clinical data and univariate logistic regression analysis of overall WMHs with different severity A total of 183 patients with none or mild overall WMHs and 163 patients with moderate to severe overall WMHs were included in this study. Comparison between groups and univariate logistic regression showed that the prevalence of age ( P < 0.001 ) and hypertension ( P < 0.001 ) in the moderate to severe overall WMHs group was higher than that in the none or mild overall WMHs group. The levels of plasma fibrinogen, Hcyt, Cystatin C and β2M in the moderate to severe overall WMHs group were higher than those in the none or mild overall WMHs group ( all P < 0.05 ). Baseline total cholesterol ( P = 0.025 ) and LDL-C ( P = 0.012 ) levels in the moderate to severe overall WMHs group were lower than those in the none or mild overall WMHs group. In the moderate to severe overall WMHs group, the proportion of patients with eGFR < 90 ml / ( min ·1.73 m2 ) was higher than that in the none or mild overall WMHs group ( P < 0.001 ) ( Table 1 ) . Table 1 : Clinical data and univariate logistic regression analysis of overall WMHs of different severities Variable None or mild group (n=183) Moderate to sever group (n=163) OR (95% CI ) P Age(years) 65.3±10.5 72.5±10.5 1.067 (1.044~1.091) ＜0.001 Male, n(%) 93(50.8) 77(47.2) 0.866(0.567~1.322) 0.506 Smoking, n(%) 51(27.9) 31(19.0) 0.608(0.366~1.010) 0.054 Drinking, n(%) 35(19.1) 20(12.3) 0.591(0.326~1.073) 0.084 Hypertension, n(%) 116(60.7) 125(80.6) 2.405(1.487~3.890) ＜0.001 Diabetes, n(%) 48(26.2) 57(35) 1.512(0.954~2.397) 0.078 Stroke history, n(%) 68(37.2) 75(46.0) 1.441(0.938~2.215) 0.096 Hyperlipidemia, n(%) 65(34) 47(30.3) 1.013(0.645~1.590) 0.956 BMI（Kg/m2) 24.3±3.3 24.1±3.5 0.984(0.925~1.048) 0.617 Fibrinogen(g/L） 3.26±0.71 3.44±0.65 1.467(1.065~2.021) 0.019 Hcyt(μmol/L) 11.5(9.50, 13.9) 13.2(10.9, 16.5) 1.060(1.017~1.104) 0.005 CysC(μmol/L) 1.10(0.98, 1.30) 1.30(1.08, 1.60) 2.770(1.551~4.948) 0.001 Total cholesterol (mmol/L) 4.27±1.01 4.08±1.02 0.782(0.631~0.969) 0.025 Triglyceride (mmol/L) 1.39(0.95, 1.85) 1.27(0.84, 1.74) 0.908(0.738~1.117) 0.359 HDL-C (mmol/L) 1.27±0.31 1.26±0.30 0.789(0.396~1.610) 0.529 LDL-C (mmol/L) 2.09(1.71, 2.69) 1.89(1.41, 2.56) 0.706(0.538~0.928) 0.012 Glycosylated hemoglobin (%) 6.1(5.80, 6.60) 6.10(5.70, 7.00) 0.982(0.927~1.040) 0.534 eGFR classification（%） 2.598(1.681~4.013) ＜0.001 ＜90ml/(min·1.73 m2) 64(35.6) 95(58.3) ≥90ml/(min·1.73 m2) 119(65.0) 68(41.7) β2M(mg/L) 2.06(1.7, 2.36) 2.44(2.00, 3.09) 2.358(1.690~3.291) ＜0.001 Clinical data and univariate Logistic regression analysis of pred-PWMHs and pred-DWMHs with different severity Among the pred-PWMHs, there were 55 patients in the mild pred-PWMHs group and 44 patients in the moderate to severe pred-PWMHs group. Compared with the mild group, the prevalence of age ( P < 0.001 ), hypertension ( P = 0.001 ), Hcyt ( P = 0.004 ) and β2M levels ( P < 0.001 ) were higher in the moderate to severe group. The level of LDL-C in moderate to severe pred-PWMHs group was lower than that in mild group ( P = 0.027 ). The proportion of patients with eGFR < 90 ml / ( min · 1.73 m2 ) in the moderate to severe pred-PWMHs group was higher than that in the mild pred-PWMHs group ( P < 0.001 ) ( Schedule 1 ). Among pred-DWMHs, there were 32 patients in mild pred-DWMHs group and 59 patients in moderate to severe pred-DWMHs group. The comparison between groups showed that the patients in the moderate to severe group were older ( P = 0.013 ), and the prevalence of diabetes ( P = 0.036 ) and the level of glycosylated hemoglobin ( P 0.05 ) ( Schedule 2 ) . Differential analysis of β2M levels in different groups of overall WMHs, pred-PWMHs and pred-DWMHs Serum β2M levels were higher in patients in the moderate to severe overall WMHs group than in the none or mild group (2.06 (1.70, 2.36) vs 2.44 (2.00，3.09) mg/L, P < 0.001); and in patients in the moderate to severe group of pred-PWMHs, serum β2M levels were higher than in the none or mild group (1.94 (1.63, 2.22) vs 2.59 (2.03, 3.12) mg/L, P < 0.001). However, the difference in β2M levels between the moderate to severe group of pred-DWMHs and the mild group was not statistically significant ( P = 0.991) ( Figure 2 ) Multifactorial Logistic Regression Analysis of Overall WMHs of Different Severities Variables with P < 0.1 in the univariate factors were included in the multivariate logistic regression equation for modeling, and the results showed that age ( OR : 1.050, 95% CI : 1.025-1.075, P < 0.001), hypertension ( OR : 2.007, 95% CI : 1.202-3.349, P = 0.008), and β2M ( OR : 1.635, 95% CI : 1.154-2.317, P =0.006) were independent risk factors for moderate to severe overall WMHs (Table 2 Model 1) . The sensitivity and specificity of β2M in predicting the severity of overall WMHs In the ROC curve of β2M predicting the severity of overall WMHs, when the cut-off value was 2.295, the sensitivity of β2M predicting moderate to severe overall WMHs was 58 %, the specificity was 75 %, AUC = 0.673 ( 95 % CI : 0.616-0.730 ) ( Fig.3 ). The corresponding cut-off value of β2M was 2.295, which was transformed into a categorical variable and included in the multivariate Logistic regression model 2 of overall WMHs. The results showed that when the serum β2M level was ≥ 2.295 mg / L, the risk of moderate to severe overall WMHs in patients would be more than doubled ( OR : 2.184, 95 % CI : 1.343-3.552, P = 0.002 ) ( Table 2 Model 2 ) . Table 2 : Multifactorial Factor Logistic Regression Analysis of overall WMHs of Different Severity Levels Variable OR 95%CI P Model 1 (Baseline β2M) Hypertension 2.007 (1.202~3.349) 0.008 Age 1.050 (1.025~1.075) ＜0.001 β2M 1.635 (1.202~3.349) 0.008 Model 2 (Baseline β2M subgroup) Hypertension 1.996 (1.192~3.343) 0.009 Age 1.053 (1.028~1.078) ＜0.001 β2M≥2.295 2.184 (1.343~3.552) 0.002 ＜2.295 Ref Multifactorial Logistic Regression Analysis of different severity levels of pred-PWMHs and pred-DWMHs Variables with P < 0.1 in the univariate factors were included in the multivariate logistic regression equations modeling pred-PWMHs, pred-DWMHs, respectively, The results showed that age ( OR : 1.079, 95 % CI : 1.028-1.132, P = 0.002 ), male ( OR : 3.722, 95 % CI : 1.213-11.41, P = 0.022 ), hypertension ( OR : 4.015, 95 % CI : 1.086-14.83, P = 0.037 ) and β2M ( OR : 3.134,95 % CI : 1.012-9.698, P = 0.048 ) were independent risk factors for moderate to severe pred-PWMHs. In pred-DWMHs, hypertension and β2M were also included as possible influencing factors in the multivariate logistic regression equation of pred-DWMHs, given that hypertension and β2M were found to be associated with overall WMHs severity in the present study, but the results showed that only age ( OR : 1.055, 95% CI : 1.011-1.102, P =0.013) was a moderate to severe independent risk factor for pred-DWMHs, and no correlation was found between β2M and pred-DWMHs (Table 3) . Table 3 : Multifactorial Logistic Regression Analysis of different severity levels of pred-PWMHs and pred-DWMHs Variable pred-PWMHs pred-DWMH P OR 95%CI P OR 95%CI Age 0.002 1.079 (1.028~1.132) 0.013 1.055 (1.011~1.102) Male 0.022 3.722 (1.213~11.41) —— —— —— Hypertension 0.037 4.015 (1.086~14.83) —— —— —— β2M 0.048 3.134 (1.012~9.698) —— —— —— Discussion In the study of risk factors for WMHs, Koohi et al. [13] used a structural equation model to screen out age and hypertension as the most important independent risk factors affecting the severity of WMHs based on 41626 subjects from the British Biobank. However, in this model, the contribution of age and hypertension to WMHs was only 16 % and 10.5 %, respectively. Therefore, the etiology of WMHs is complex, and many potential causes need to be found to explain. This study also found that age and hypertension were independent risk factors for WMHs, supporting the above conclusions. In previous studies, kidney-related biomarkers such as serum creatinine, eGFR, urinary protein, and cystatin C have been found to be associated with the severity of WMHs [13,14] . This study found that eGFR, Cystatin C and β2M were different in different severity of WMHs, but only β2M level was an independent risk factor for moderate to severe WMHs. The difference between the above results may be related to the fact that the subjects were from CSVD rather than CKD patients, and it also supports that β2M may be a more sensitive biomarker of renal function than eGFR and cystatin C [6] . This study shows that the serum β2M level of moderate to severe WMHs is higher than that of none or mild group, which verifies the previous research results [9] . β2M is a small molecule protein with a molecular weight of approximately 11.8 KDa, which is widely present on the surface of nucleated cells and is involved in the composition of major histocompatibility complex class I molecules (MHC I), which is essential for MHC I to perform its normal antigen-presenting function [15] . In normal physiological processes, β2M is interpreted into the circulation with the cell division, and small molecules of β2M can freely pass through the glomerular filtration membrane, but 99 % is reabsorbed at the proximal convoluted tubules. Unlike serum creatinine, which is affected by muscle metabolism, the variability of serum β2M level is low, and its fluctuation is more sensitive than eGFR estimated based on serum creatinine [16] . Therefore, β2M can reflect its early predictive value in CKD and CSVD with similar pathophysiological background of small vessels. On the other hand, because free small molecule β2M is easy to cross the blood-brain barrier [17] ，it may also be directly involved in the process of central nervous system diseases. In a cross-sectional study of 387 healthy people of different age groups, β2M levels increased with age and were associated with aging-related heart, kidney, and liver metabolic dysfunction [18] . In the study of aging mechanism, β2M was found to negatively regulate the regeneration function of hippocampal neurons with the increase of age of experimental mice, resulting in cognitive impairment. Therefore, β2M is also considered as one of the possible pro-aging factors [19] . In cerebrovascular disease, the level of serum β2M in patients with ischemic stroke is higher than that in hemorrhagic stroke or healthy controls, which is an independent risk factor for ischemic stroke and is associated with a high risk of recurrence [20] . In conclusion, WMHs is a small vascular disease closely related to aging, and it is of great significance to understand how β2M participates in the production of WMHs. The specific mechanism by which β2M affects the severity of WMHs remains unclear. As mentioned above, β2M is elevated in patients with ischemic stroke. This phenomenon also exists in the middle cerebral artery occlusion ( MCAO ) mouse stroke model. The protein expression levels of MHC I molecule and β2M in the lesion side of the model were significantly higher than those in the undamaged contralateral hemisphere and the sham operation control group [21] . Whereas, MHC I molecule is present in the plasma membrane of axons and dendrites prior to full synaptic maturation during neuronal repair or ontogeny and negatively regulate the density and normal function of synaptic connections in glutamatergic and γ-aminobutyric acidergic neurons [22] . In the above process, the highly expressed β2M binds to the MHC I heavy chain through non-covalent bonds and participates in stabilizing its configuration to exert negative biological effects [21] . This injury mechanism needs to be paid attention to in WMHs with hypoxic-ischemic injury. Chronic hemodynamic changes secondary to atherosclerosis caused by traditional risk factors such as hypertension and lipid metabolism disorders, and insufficient cerebral perfusion are also one of the reasons for increasing the burden of WMHs [23] . Studies have shown that atherosclerosis is also a chronic inflammatory vascular disease involving vascular endothelial cells and immune cells [24] . Accompanying chronic, low-grade inflammatory responses, innate and acquired immune cells are enriched in atherosclerotic plaque tissues, where monocytes play a role in plaque progression by inducing phenotypic switching of vascular smooth muscle cells through NLRP3 inflammatory vesicle activation [25] . A recent study found that abnormal aggregation of β2M can activate NLRP3 inflammasome, leading to excessive secretion of IL-1β and IL-18, thereby promoting the progression of multiple myeloma [26] . Chen et al. [27] also found that the levels of β2M, NLRP3 inflammasome and pro-inflammatory factors ( IL-1β, IL-6 and TNF ) in ischemic brain tissue were significantly increased in the MCAO model of rats. After knocking out the β2M gene, the levels of NLRP3 inflammasome and pro-inflammatory factors were significantly reduced. The above studies provide some evidence that β2M may participate in the pathological process of WMHs as a pro-inflammatory factor by activating the NLRP3 inflammasome pathway. It should be noted that both the brain and kidney are parenchymal organs with continuous perfusion of high blood volume and have similar hemodynamic characteristics. Compared with the limited volume and mass in the human body, the brain and kidney receive about 45 % of the total cardiac output [28,29] . Even if there is a large blood pressure fluctuation within the physiological range, the brain and renal vessels need to maintain a strong vascular tension, providing a constant perfusion pressure from the main blood vessels, perforating vessels to small blood vessels, to meet the brain 's vigorous energy needs and kidney filtration needs [30] . When hypertension, diabetes, aging and other factors lead to vascular injury and decreased compliance, the brain and kidney reflect the decompensation of blood perfusion and vascular regulation in the form of small vascular disease. At this time, β2M increased due to decreased glomerular filtration function ; therefore, β2M itself is a common biomarker for small vessel disease. PWMHs and DWMHs often coexist in the occurrence and progression of WMHs. However, studies have shown that in addition to distributional differences, these two WMHs types have different histopathological manifestations, suggesting that they are partially different in pathogenesis, with PWMHs likely to be more involved in cerebrospinal fluid leakage and inflammatory processes [31] , Chronic ischemia and hypoxia play a greater role in the formation of DWMHs [32] . As an inflammatory marker that can respond to vascular inflammation and endothelial dysfunction, Hcyt was found to be only associated with PWMHs [12] . Autosomal dominant cerebral artery disease ( CADASIL ) with subcortical infarction and leukoencephalopathy, as a hereditary cerebral small vessel disease caused by Notch3 gene mutation, although its white matter lesions also involve periventricular, its characteristic early imaging changes are WMHs in deep white matter regions such as frontal, temporal lobe and external capsule [33] . Based on the possible differences between PWMHs and DWMHs, this study preliminarily explored the relationship between β2M and the two respectively. The results showed that β2M was still associated with moderate to severe PWMHs except for age, male, and hypertension. However, in DWMHs, β2M and other factors did not show correlation except for age. There is no previous study on the difference in the effect of β2M on WMHs in different parts. It is speculated that it may be similar to Hcyt and produce effects through inflammatory processes. On the other hand, this result provides partial evidence for the existence of some pathophysiologic background differences between PWMHs and DWMHs. Regarding hypertension, a traditional risk factor clearly identified in overall WMHs, the correlation with DWMHs was not confirmed in this study, in agreement with the results of Griffanti [34] , in whose study hypertension was only associated with PWMHs and did not show a correlation with DWMHs; in addition to a possible heterogeneity of WMHs, this result needs to be confirmed by a high-quality study. There are still some shortcomings in this study : First, as a single-center cross-sectional study, it is impossible to provide more information to make causal inferences about the relationship between β2M and WMHs. Secondly, according to the difference of Fazekas score ≥ 1, it is not possible to distinguish PWMHs and DWMHs patients with high quality. In this study, 45 % of the brain WMHs did not have a site advantage. Although pred-PWMHs and pred-DWMHs also preliminarily revealed the difference in the predictive value of β2M for different parts of WMHs, the results still need to be interpreted carefully. With the development of imaging technology, the results of this study need to be further verified on the basis of accurate evaluation of WMHs. Conclusion Our findings suggest that β2M is an independent risk factor for moderate to severe overall WMHs and moderate to severe pred-PWMHs, and it may be a potential biomarker for assessing the severity of WMHs. The risk of overall WMHs severity was increased when the β2M≥ value was 2.295. Declarations Acknowledgements Not applicable Authors’ contributions Fei Wang: Conceived, designed, and implemented the study; collected, analyzed, and interpreted the data; performed statistical analysis; and wrote the paper. Tingting Liu: Collected, analyzed, and interpreted the data; performed statistical analysis. Jun He and Mingwu Xia: Obtained research funding; conceived, designed, and directed the study; analyzed and interpreted the data; revised the paper. Rongfeng Wang: Obtained research funding; conceived, designed, and directed the study; analyzed and interpreted the data; performed statistical analysis; revised the thesis. Funding This study was funded by a grant from the Applied Medical Research Program, Hefei Municipal Health and Wellness Committee (Hwk2022zd004). Data availability The datasets generated during the current study are available from the corresponding author on reason able request. The data are not publicly available due to privacy or ethical restrictions. Ethics approval and consent to participate This study involving human participants were reviewed and approved by Ethics Committee of the Second People’s Hospital of Hefei City(NO.2022-S077). All methods were performed in accordance with the Declaration of Helsinki and the relevant guidelines and regulations. Informed consent to participate was waived by the Ethics Committee of Hefei Second People's Hospital Affiliated to Bengbu Medical University due to the retrospective nature of the study design. Consent for publication Not applicable Conflict of interest The authors declare that they have no conficts of interest References Das AS, Regenhardt RW, Vernooij MW, et al. Asymptomatic Cerebral Small Vessel Disease: Insights from Population-Based Studies[J]. J Stroke. 2019;21(2):121–38. 10.5853/jos.2018.03608 . De Havenon A, Smith EE, Sharma R, et al. Improvement in the Prediction of Cerebrovascular Events With White Matter Hyperintensity[J]. J Am Heart Association. 2023;12(13):e029374. 10.1161/JAHA.123.029374 . Gouw AA, Seewann A, Van Der Flier WM, et al. Heterogeneity of small vessel disease: a systematic review of MRI and histopathology correlations[J]. J Neurol Neurosurg Psychiatry. 2011;82(2):126–35. 10.1136/jnnp.2009.204685 . Yao T, Song G, Li Y, et al. Chronic kidney disease correlates with MRI findings of cerebral small vessel disease[J]. Ren Fail. 2021;43(1):255–63. 10.1080/0886022X.2021.1873804 . Li L, Dong M, Wang XG. The Implication and Significance of Beta 2 Microglobulin: A Conservative Multifunctional Regulator[J]. Chin Med J. 2016;129(4):448–55. 10.4103/0366-6999.176084 . Jin YX, Zhang S, Xiao J, et al. Association between serum β2-microglobulin levels and the risk of all-cause and cardiovascular disease mortality in chinese patients undergoing maintenance hemodialysis[J]. BMC Nephrol. 2023;24(1):170. 10.1186/s12882-023-03191-5 . Liu ZY, Tang F, Yang JZ, et al. The Role of Beta2-Microglobulin in Central Nervous System Disease[J]. Cell Mol Neurobiol. 2024;44(1):46. 10.1007/s10571-024-01481-6 . Hu FY, Wu W, Liu Q, et al. β2-Microglobulin is a Novel and Reliable Biomarker for Predicting Ischemic Stroke Recurrence: A Prospective Cohort Study[J]. Front Pharmacol. 2022;13:916769. 10.3389/fphar.2022.916769 . Liu X, Leng Hl et al. Role of elevated serum β2 microglobulin on cerebral white matter damage in patients with cerebral small vessel disease[J]. J Med Student Res 2022.35(6): 612–610.16571/j.cnki.1008-8199.2022.06.008 Duering M, Biessels GJ, Brodtmann A, et al. Neuroimaging standards for research into small vessel disease—advances since 2013[J]. Lancet Neurol. 2023;22(7):602–18. 10.1016/S1474-4422(23)00131-X . Fazekas F, Chawluk J, Alavi A, et al. MR Signal Abnormalities at 1.5 T in Alzheimer’s Dementia and Normal Aging[J]. AJR Am J Roentgenol. 1987;149(2):351–6. 10.2214/ajr.149.2.351 . Lee KO, Woo MH, Chung D, et al. Differential Impact of Plasma Homocysteine Levels on the Periventricular and Subcortical White Matter Hyperintensities on the Brain[J]. Front Neurol. 2019;10:1174. 10.3389/fneur.2019.01174 . Koohi F, Harshfield EL, Markus HS. Contribution of Conventional Cardiovascular Risk Factors to Brain White Matter Hyperintensities[J]. J Am Heart Association. 2023;12(14):e030676. 10.1161/JAHA.123.030676 . Greco F, Quarta LG, Parizel PM, et al. Relationship between chronic kidney disease and cerebral white matter hyperintensities: a systematic review[J]. Quant Imaging Med Surg. 2023;13(11). 10.21037/qims-22-707 . Sivanathan PC, Ooi KS, Mohammad Haniff M. Lifting the Veil: Characteristics, Clinical Significance, and Application of β-2-Microglobulin as Biomarkers and Its Detection with Biosensors[J]. ACS Biomater Sci Eng. 2022;8(8):3142–61. 10.1021/acsbiomaterials.2c00036 . Selvin E, Juraschek SP, Eckfeldt J, et al. Within-Person Variability in Kidney Measures[J]. Am J Kidney Dis. 2013;61(5):716–22. 10.1053/j.ajkd.2012.11.048 . Gao Y, Hong Y, Huang L, et al. β2-microglobulin functions as an endogenous NMDAR antagonist to impair synaptic function[J]. Cell. 2023;186(5):1026–e103820. 10.1016/j.cell.2023.01.021 . Dong X, Cai R, Yang F, et al. Predictive value of plasma β2-microglobulin on human body function and senescence[J]. Eur Rev Med Pharmacol Sci. 2016;20(11):2350–6. Smith LK, He Y, Park JS, et al. β2-microglobulin is a systemic pro-aging factor that impairs cognitive function and neurogenesis[J]. Nat Med. 2015;21(8):932–7. 10.1038/nm.3898 . Qun S, Hu F, Wang G, et al. Serum beta2-microglobulin levels are highly associated with the risk of acute ischemic stroke[J]. Sci Rep. 2019;9(1):6883. 10.1038/s41598-019-43370-9 . Adelson JD, Barreto G, Xu L. Neuroprotection from Stroke in the Absence of MHCI or PirB[J]. Neuron. 2012;73(6):1100–7. 10.1016/j.neuron.2012.01.020 . Glynn MW, Elmer BM, Garay PA, et al. MHCI negatively regulates synapse density during the establishment of cortical connections[J]. Nat Neurosci. 2011;14(4):442–51. 10.1038/nn.2764 . Bernbaum M, Menon BK, Fick G, et al. Reduced Blood Flow in Normal White Matter Predicts Development of Leukoaraiosis[J]. J Cereb Blood Flow Metabolism. 2015;35(10):1610–5. 10.1038/jcbfm.2015.92 . Soehnlein O, Libby P. Targeting inflammation in atherosclerosis — from experimental insights to the clinic[J]. Nat Rev Drug Discovery. 2021;20(8):589–610. 10.1038/s41573-021-00198-1 . Burger F, Baptista D, Roth A, et al. NLRP3 Inflammasome Activation Controls Vascular Smooth Muscle Cells Phenotypic Switch in Atherosclerosis[J]. Int J Mol Sci. 2021;23(1):340. 10.3390/ijms23010340 . Hofbauer D, Mougiakakos D, Broggini L, et al. β2-microglobulin triggers NLRP3 inflammasome activation in tumor-associated macrophages to promote multiple myeloma progression[J]. Immunity. 2021;54(8):1772–e17879. 10.1016/j.immuni.2021.07.002 . Chen F, Liu J, Li FQ, et al. β2-Microglobulin exacerbates neuroinflammation, brain damage, and cognitive impairment after stroke in rats[J]. Neural Regeneration Res. 2023;18(3):603. 10.4103/1673-5374.350204 . Xing CY, Tarumi T, Liu J, et al. Distribution of cardiac output to the brain across the adult lifespan[J]. J Cereb Blood Flow Metabolism. 2017;37(8):2848–56. 10.1177/0271678X16676826 . Chapman CL, Johnson BD, Parker MD, et al. Kidney physiology and pathophysiology during heat stress and the modification by exercise, dehydration, heat acclimation and aging[J]. Temp (Austin). 2020;8(2):108–59. 10.1080/23328940.2020.1826841 . Rajamani K. The Cerebro-Renal System- Anatomical and Physiological Considerations[J]. J Stroke Cerebrovasc Dis. 2021;30(9):105541. 10.1016/j.jstrokecerebrovasdis.2020.105541 . Schmidt R, Schmidt H, Haybaeck J, et al. Heterogeneity in age-related white matter changes[J]. Acta Neuropathol. 2011;122(2):171–85. 10.1007/s00401-011-0851-x . Fernando MS, Simpson JE, Matthews F, et al. White Matter Lesions in an Unselected Cohort of the Elderly: Molecular Pathology Suggests Origin From Chronic Hypoperfusion Injury[J]. Stroke. 2006;37(6):1391–8. 10.1161/01.STR.0000221308.94473.14 . O’Sullivan M, Jarosz JM, Martin RJ, et al. MRI hyperintensities of the temporal lobe and external capsule in patients with[J]. Neurology. 2001;56(5):628–34. 10.1212/wnl.56.5.628 . Griffanti L, Jenkinson M, Suri S, et al. Classification and characterization of periventricular and deep white matter hyperintensities on MRI: A study in older adults[J]. NeuroImage. 2018;170:174–81. 10.1016/j.neuroimage.2017.03.024 . Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterialschedules.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6284537","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":446999663,"identity":"f5deaa6b-8f42-4949-b662-de558f6877b4","order_by":0,"name":"Wang Fei","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Fei","suffix":""},{"id":446999665,"identity":"4409b27a-77b9-4445-8441-778eeb7ce020","order_by":1,"name":"Liu Tingting","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Liu","middleName":"","lastName":"Tingting","suffix":""},{"id":446999667,"identity":"6d5d7d3c-d94d-4ede-b58d-bd3fad0e9ba5","order_by":2,"name":"He Jun","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Jun","suffix":""},{"id":446999668,"identity":"fbbe5b8f-17f6-496d-b35c-be32fd2cb12a","order_by":3,"name":"Xia Mingwu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Mingwu","suffix":""},{"id":446999669,"identity":"432c65ea-917c-4a5a-81e7-fee1fb89ee14","order_by":4,"name":"Wang Rongfeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYFACxgcHPhhIyLGxtx8gVguz4cEZBRbGfDxnEojWYnyY40NF4jwJBwPiNBjcPsxwmMFAIr1NgiGB4UfFNiK0nEtmOFxgIJHbJt14gLHnzG3CWszO8B84PAOkReZAAjNjG1FamBkO8wAdxiaRYECalgTitdgDtRwEOsywDRjIB4nyi2QPM/OHD3/q5OXb2w8++FFBhBYUcIBE9aNgFIyCUTAKcAEAo307aST+BsgAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Wang","middleName":"","lastName":"Rongfeng","suffix":""}],"badges":[],"createdAt":"2025-03-22 15:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6284537/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6284537/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82118172,"identity":"dc7e1871-247f-441d-a1ab-c7fb988c83f4","added_by":"auto","created_at":"2025-05-07 03:05:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46120,"visible":true,"origin":"","legend":"\u003cp\u003eEnrollment Flow Chart for Study Subjects\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6284537/v1/5df3219e06b3fa6cc861ae7d.png"},{"id":82120101,"identity":"f2271756-1622-47be-8ac2-85f1ce9ead83","added_by":"auto","created_at":"2025-05-07 03:13:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":109247,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential analysis of β2M levels in different groups of overall WMHs, pred-PWMHs and pred-DWMHs\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6284537/v1/8699e35aa11be90929a96ea4.png"},{"id":82122510,"identity":"b477a2cb-f71b-41ac-9f89-cfc0cc76424a","added_by":"auto","created_at":"2025-05-07 03:29:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53500,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis of β2M\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6284537/v1/7d1e6dab1423da121da87df3.png"},{"id":100785809,"identity":"98a633be-a4a7-4446-9694-59c2e014e7a3","added_by":"auto","created_at":"2026-01-21 11:58:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1091784,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6284537/v1/79777ee4-54e6-4cec-b2a5-d3914d4d51e6.pdf"},{"id":82120098,"identity":"ab821343-150c-429b-a241-16c89f06bf2c","added_by":"auto","created_at":"2025-05-07 03:13:43","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":96768,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterialschedules.doc","url":"https://assets-eu.researchsquare.com/files/rs-6284537/v1/959117c74c0419430cd84e49.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association and predictive value analysis of β2-microglobulin and the severity of white matter hyperintensities","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWhite Matter Hyperintensities (WMHs) are the most common subtype of Cerebral Small Vessel Disease (CSVD) in the elderly population. A community-based prevalence survey of people over 60 years of age found that WMHs were present in 65%-96% of the respondents\u0026nbsp;\u003csup\u003e[1]\u003c/sup\u003e. WMHs are a well-established risk factor for both stroke and cognitive impairment, and a recent study demonstrated that common cerebrovascular disease risk factors, such as hypertension, indirectly mediated the development of stroke and cognitive impairment in 26.3% of patients through WMHs by mediation effect analysis\u003csup\u003e[2]\u003c/sup\u003e. The development of WMHs may involve multiple pathophysiological mechanisms, including impaired cerebral blood flow autoregulation, blood-brain barrier damage, dysfunction of small vessel endothelium, oxidative stress, and inflammation\u003csup\u003e[3]\u003c/sup\u003e. The search for potential biological markers behind different mechanisms may be important for the stratification of WMHs, early prevention and selection of interventions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCSVD and chronic kidney disease (CKD) are both chronic progressive diseases resulting from damage to the small blood vessels of parenchymal organs or their collateral blood and body fluid barriers, and they may share a similar pathophysiologic background. Decreased Estimated Glomerular Filtration Rate (eGFR) has been found to correlate with the severity of WMHs\u003csup\u003e[4]\u003c/sup\u003e. \u0026beta;2 -Microglobulin ( \u0026beta;2M ), \u0026nbsp;a new endogenous renal biomarker, has been recently identified and is more sensitive than eGFR in the assessment of glomerular filtration function. It is more sensitive to the evaluation of glomerular filtration function than eGFR\u003csup\u003e[5]\u003c/sup\u003e.\u0026nbsp;Elevated serum\u0026nbsp;\u0026beta;2M is not only associated with an increased risk of cardiovascular events and all-cause mortality in dialysis patients with CKD\u0026nbsp;\u003csup\u003e[6]\u003c/sup\u003e,but also has been found to be associated with neurological diseases such as stroke\u0026nbsp;\u003csup\u003e[7]\u003c/sup\u003e. Recent studies have demonstrated that elevated \u0026beta;2M is linked to atherosclerosis\u0026nbsp;\u003csup\u003e[6]\u003c/sup\u003e,\u0026nbsp;and serves as an independent predictor of ischemic stroke recurrence\u0026nbsp;\u003csup\u003e[8]\u003c/sup\u003e.\u0026nbsp;Whether \u0026beta;2M\u0026nbsp;can be used as a reliable biological marker to reflect the severity of cerebral small-vessel pathology deserves to be further explored. A recent observational study confirmed the predictive value of \u0026beta;2M for the severity of WMHs, but did not assess eGFR levels or their potential impact on the predictive value of \u0026beta;2M\u0026nbsp;\u003csup\u003e[9]\u003c/sup\u003e. In this study, we examined baseline blood \u0026beta;2M levels and eGFR and other related clinical data in patients with WMHs to investigate their correlation and predictive value with periventricular white matter high signals (PWMHs), deep white matter high signals (DWMHs), and the overall WMHs severity, respectively.\u003c/p\u003e"},{"header":"Participants and methods","content":"\u003cp\u003e\u003cstrong\u003eResearch object\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsecutively, patients who were hospitalized in the Department of Neurology of the Second People\u0026apos;s Hospital of Hefei City from December 2021 to April 2024 and were clearly qualified for the subtype of cerebral small vessel disease WMHs by MRI were collected. Inclusion criteria: a. The age of the enrolled patients was\u0026nbsp;\u0026ge;40 years old; b. WMHs cranial MRI conformed to the manifestation of WMHs subtype in the CSVD imaging criteria (standards for reporting vascular changes on neuroimaging, STRIVE)\u003csup\u003e[10]\u003c/sup\u003e; c. Patients with comorbid central nervous system infections, tumors, and hematologic disorders were excluded; and d. Patients with possible cerebral amyloid angiopathy, hereditary, inflammatory, and immune-mediated cerebral small-vessel disease were excluded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterial gathering\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic information, cerebrovascular risk factors (including Smoking,\u0026nbsp;Drinking, Hypertension,\u0026nbsp;Diabetes, Stroke history, or transient ischemic attack), and body mass index were collected from all enrolled patients. On the second day of admission, routine venous blood samples were taken for biochemical analysis, including measurements of Coagulation parameters, Glycated hemoglobin, Creatinine, Urea nitrogen, Total cholesterol, Triglyceride, Low-density lipoprotein- cholesterol(LDL-C), High-density lipoprotein-cholesterol(HDL-C), Cystatin C(CysC), Homocysteine(Hcyt), Fibrinogen, and \u0026beta;2-microglobulin (\u0026beta;2M). estimated Glomerular Filtration Rate (eGFR) was calculated using the simplified MDRD formula; an eGFR \u0026lt;90 ml/(min\u0026middot;1.73 m\u0026sup2;) indicated impaired glomerular filtration. The \u0026beta;2M level was measured by radioimmunoassay, with a reference range of 1.3\u0026ndash;2.7 mg/L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImaging Examination\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBrain MRI images were performed using a Siemens 1.5T magnetic resonance scanner (Model: Avanto I Class). The sequences included T1WI, T2WI, Fluid-Attenuated Inversion Recovery (FLAIR) and\u0026nbsp;Diffusion-Weighted Imaging\u0026nbsp;( DWI ). Annet medical image management system was used to analyze the MRI imaging findings of patients, and the severity of WMHs in different parts was recorded. WMHs were defined as T2WI and FLAIR high-signal and T1WI equal or low-signal lesions in the periventricular or deep subcortical white matter regions of the lateral ventricles\u0026nbsp;\u003csup\u003e[10]\u003c/sup\u003e. According to the Fazekas scale: paraventricular and deep white matter high signal was assessed, and the two sites were summed to obtain a total score (a total Fazekas score of 0-2 was categorized as\u0026nbsp;none\u0026nbsp;or mild WMHs, and 3-6 was categorized as\u0026nbsp;moderate to severe\u0026nbsp;WMHs)\u0026nbsp;\u003csup\u003e[11]\u003c/sup\u003e. Given that periventricular and deep WMHs often occur simultaneously, in order to investigate the risk factors for PWMHs and DWMHs separately; in this study, with reference to the literature\u0026nbsp;\u003csup\u003e[12]\u003c/sup\u003e, According to the difference between the periventricular and deep Fazekas scores\u0026nbsp;\u0026ge;\u0026nbsp;1, the patients were divided into the predominantly periventricular white matter high signal (pred-PWMHs ) subgroup and the predominantly deep white matter high signal (pred-DWMHs ) subgroup. Each subgroup was further divided into mild ( Fazekas score 1 ) and moderate to severe group ( Fazekas score 2-3 ) according to the Fazekas scale. MRI image data were blindly evaluated by two qualified physicians above the deputy director of neurology. If the results were inconsistent, they were consistent through consultation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were conducted by SPSS 25.0 software package (SPSS Inc., Chicago, IL, USA). quantitative information that conformed to normal distribution was expressed as mean \u0026plusmn; standard deviation (\u003cimg width=\"8\" height=\"16\" src=\"data:image/png;base64,R0lGODlhCAAQAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAwAIAAoAhAAAAAAAAAA6kDoAADoAOjo6OjpmkDpmtjqQ22YAAGZmtmaQ22a2/5A6AJBmtpDb/7ZmANuQZtu2ZtvbkNv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwECAwECAwUoYCCOAWCeaHpWwwFExIM5kjAt6JUYFIo1jFSlgEBZFLUbACKg6JqAEAA7\" alt=\"image\"\u003e\u0026plusmn;s), and quantitative information that did not conform to normal distribution was expressed as median and quartile (Q) of Q25 and Q75, and comparisons between groups were made Independent samples t-test or Mann-Whitney U rank-sum test was used; qualitative data were expressed as frequencies and percentages (%), and comparisons between groups were made using the chi-square test or Fisher\u0026apos;s exact probability method. Variables with\u003cem\u003e\u0026nbsp;P\u003c/em\u003e \u0026lt; 0.1 in the univariate logistic regression analysis were included in the multivariate logistic regression model, and the odds ratio ( \u003cem\u003eOR\u003c/em\u003e ) and 95 % confidence interval ( \u003cem\u003eCI\u0026nbsp;\u003c/em\u003e) were calculated. Receiver operating characteristic curve ( ROC ) analysis was performed on\u0026nbsp;\u0026beta;2M, and \u0026nbsp;Area Under Curve ( AUC ) as well as the bounding value were calculated. \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn this study, 420 patients who initially met the inclusion criteria were identified. After screening to exclude individuals who did not meet the criteria, 346 patients were ultimately included in the final analysis \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Figure 1\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe mean age was 68.7\u0026plusmn;11.1 years, of which 170 were male and 47.1% had moderate to severe WMHs. A total of 99 cases of pred-PWMHs and 91 cases of pred-DWMHs were entered into the inter-subgroup comparison, of which the proportion of moderate-to-severe pred-PWMHs and pred-DWMHs were 44.4% and 64.8%, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical data and univariate logistic regression analysis of overall WMHs with different severity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 183 patients with none or mild overall WMHs and 163 patients with moderate to severe overall WMHs were included in this study. Comparison between groups and univariate logistic regression showed that the prevalence of age ( \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001 ) and hypertension (\u003cem\u003e\u0026nbsp;P\u003c/em\u003e \u0026lt; 0.001 ) in the moderate to severe overall WMHs group was higher than that in the none or mild overall WMHs group. The levels of plasma fibrinogen, Hcyt, Cystatin C and \u0026beta;2M in the moderate to severe overall WMHs group were higher than those in the none or mild overall WMHs group ( all \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05 ). Baseline total cholesterol ( \u003cem\u003eP\u003c/em\u003e = 0.025 ) and LDL-C ( \u003cem\u003eP\u003c/em\u003e = 0.012 ) levels in the moderate to severe overall WMHs group were lower than those in the none or mild overall WMHs group. In the moderate to severe overall WMHs group, the proportion of patients with eGFR \u0026lt; 90 ml / ( min \u0026middot;1.73 m2 ) was higher than that in the none or mild overall WMHs group ( \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 ) \u003cstrong\u003e( Table 1 )\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e: Clinical data and univariate logistic regression analysis of overall WMHs of different severities\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"88%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNone or mild group\u003c/p\u003e\n \u003cp\u003e(n=183)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eModerate to sever group\u003c/p\u003e\n \u003cp\u003e(n=163)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e(95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e65.3\u0026plusmn;10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e72.5\u0026plusmn;10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.067 (1.044~1.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eMale, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e93(50.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e77(47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.866(0.567~1.322)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eSmoking, n(%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e51(27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e31(19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.608(0.366~1.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eDrinking, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e35(19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e20(12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.591(0.326~1.073)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eHypertension, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e116(60.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e125(80.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.405(1.487~3.890)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eDiabetes, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e48(26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e57(35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.512(0.954~2.397)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eStroke history, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e68(37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e75(46.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.441(0.938~2.215)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eHyperlipidemia, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e65(34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e47(30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.013(0.645~1.590)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eBMI（Kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e24.3\u0026plusmn;3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e24.1\u0026plusmn;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.984(0.925~1.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eFibrinogen(g/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3.26\u0026plusmn;0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.44\u0026plusmn;0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.467(1.065~2.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eHcyt(\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e11.5(9.50, 13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e13.2(10.9, 16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.060(1.017~1.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eCysC(\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.10(0.98, 1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.30(1.08, 1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.770(1.551~4.948)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eTotal cholesterol (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4.27\u0026plusmn;1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4.08\u0026plusmn;1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.782(0.631~0.969)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eTriglyceride (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.39(0.95, 1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.27(0.84, 1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.908(0.738~1.117)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.27\u0026plusmn;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.26\u0026plusmn;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.789(0.396~1.610)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.09(1.71, 2.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.89(1.41, 2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.706(0.538~0.928)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eGlycosylated hemoglobin (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6.1(5.80, 6.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e6.10(5.70, 7.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.982(0.927~1.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eeGFR classification（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.598(1.681~4.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e＜90ml/(min\u0026middot;1.73 m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e64(35.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e95(58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 104px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026ge;90ml/(min\u0026middot;1.73 m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e119(65.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e68(41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026beta;2M(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.06(1.7, 2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2.44(2.00, 3.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.358(1.690~3.291)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical data and univariate Logistic regression analysis of pred-PWMHs and pred-DWMHs with different severity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the pred-PWMHs, there were 55 patients in the mild pred-PWMHs group and 44 patients in the moderate to severe pred-PWMHs group. Compared with the mild group, the prevalence of age ( \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 ), hypertension (\u003cem\u003e\u0026nbsp;P\u003c/em\u003e = 0.001 ), Hcyt ( \u003cem\u003eP\u003c/em\u003e = 0.004 ) and \u0026beta;2M levels ( \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 ) were higher in the moderate to severe group. The level of LDL-C in moderate to severe pred-PWMHs group was lower than that in mild group ( \u003cem\u003eP\u003c/em\u003e = 0.027 ). The proportion of patients with eGFR \u0026lt; 90 ml / ( min \u0026middot; 1.73 m2 ) in the moderate to severe pred-PWMHs group was higher than that in the mild pred-PWMHs group ( \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001 ) \u003cstrong\u003e( Schedule 1 ).\u0026nbsp;\u003c/strong\u003eAmong pred-DWMHs, there were 32 patients in mild pred-DWMHs group and 59 patients in moderate to severe pred-DWMHs group. The comparison between groups showed that the patients in the moderate to severe group were older (\u003cem\u003e\u0026nbsp;P\u003c/em\u003e = 0.013 ), and the prevalence of diabetes ( \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.036 ) and the level of glycosylated hemoglobin (\u003cem\u003e\u0026nbsp;P\u0026nbsp;\u003c/em\u003e\u0026lt; 0.048 ) were higher than those in the mild group. There was no difference in the prevalence of hypertension and the level of \u0026beta;2M between the two groups ( \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05 )\u003cstrong\u003e\u0026nbsp;( Schedule 2 )\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential analysis of \u0026beta;2M levels in different groups of overall WMHs, pred-PWMHs and pred-DWMHs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum \u0026beta;2M levels were higher in patients in the moderate to severe overall WMHs group than in the none or mild group (2.06 (1.70, 2.36) vs 2.44 (2.00，3.09) mg/L, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001); and in patients in the moderate to severe group of pred-PWMHs, serum\u0026nbsp;\u0026beta;2M\u0026nbsp;levels were higher than in the\u0026nbsp;none\u0026nbsp;or mild group (1.94 (1.63, 2.22) vs 2.59 (2.03, 3.12) mg/L, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). However, the difference in\u0026nbsp;\u0026beta;2M\u0026nbsp;levels between the moderate to severe group of pred-DWMHs and the mild group was not statistically significant (\u003cem\u003eP\u003c/em\u003e = 0.991) \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Figure 2\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultifactorial Logistic Regression Analysis of Overall WMHs of Different Severities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVariables with \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.1 in the univariate factors were included in the multivariate logistic regression equation for modeling, and the results showed that age (\u003cem\u003eOR\u003c/em\u003e: 1.050, 95% \u003cem\u003eCI\u003c/em\u003e: 1.025-1.075, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), hypertension (\u003cem\u003eOR\u003c/em\u003e: 2.007, 95%\u003cem\u003e\u0026nbsp;CI\u003c/em\u003e: 1.202-3.349, \u003cem\u003eP\u003c/em\u003e = 0.008), and \u0026beta;2M (\u003cem\u003eOR\u003c/em\u003e: 1.635, 95% \u003cem\u003eCI\u003c/em\u003e: 1.154-2.317,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e=0.006) were independent risk factors for moderate to severe overall WMHs \u003cstrong\u003e(Table 2 Model 1)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe sensitivity and specificity of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026beta;2M\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;in predicting the severity of overall WMHs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the ROC curve of \u0026beta;2M predicting the severity of overall WMHs, when the cut-off value was 2.295, the sensitivity of \u0026beta;2M predicting moderate to severe overall WMHs was 58 %, the specificity was 75 %, AUC = 0.673 ( 95 % \u003cem\u003eCI\u003c/em\u003e : 0.616-0.730 ) (\u003cstrong\u003e\u0026nbsp;Fig.3\u003c/strong\u003e ). The corresponding cut-off value of \u0026beta;2M was 2.295, which was transformed into a categorical variable and included in the multivariate Logistic regression model 2 of overall WMHs. The results showed that when the serum \u0026beta;2M level was\u0026nbsp;\u0026ge;\u0026nbsp;2.295 mg / L, the risk of moderate to severe overall WMHs in patients would be more than doubled ( \u003cem\u003eOR\u003c/em\u003e : 2.184, 95 % \u003cem\u003eCI\u003c/em\u003e : 1.343-3.552, \u003cem\u003eP\u003c/em\u003e = 0.002 )\u003cstrong\u003e\u0026nbsp;( Table 2 Model 2 )\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: Multifactorial Factor Logistic Regression Analysis of overall WMHs of Different Severity Levels\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"428\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u0026nbsp;\u003c/strong\u003e(Baseline \u0026beta;2M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e(1.202~3.349)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e(1.025~1.075)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026beta;2M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e(1.202~3.349)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u0026nbsp;\u003c/strong\u003e(Baseline \u0026beta;2M\u0026nbsp;subgroup)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e(1.192~3.343)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e(1.028~1.078)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026beta;2M\u0026ge;2.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e(1.343~3.552)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;＜2.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultifactorial Logistic Regression Analysis of different severity levels of pred-PWMHs and pred-DWMHs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVariables with \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.1 in the univariate factors were included in the multivariate logistic regression equations modeling pred-PWMHs, pred-DWMHs, respectively, The results showed that age ( \u003cem\u003eOR\u003c/em\u003e : 1.079, 95 % \u003cem\u003eCI\u003c/em\u003e : 1.028-1.132,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e = 0.002 ), male ( \u003cem\u003eOR\u003c/em\u003e : 3.722, 95 % \u003cem\u003eCI\u003c/em\u003e : 1.213-11.41,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e = 0.022 ), hypertension ( \u003cem\u003eOR\u003c/em\u003e : 4.015, 95 % \u003cem\u003eCI\u003c/em\u003e : 1.086-14.83, \u003cem\u003eP\u003c/em\u003e = 0.037 ) and \u0026beta;2M (\u003cem\u003e\u0026nbsp;OR\u003c/em\u003e : 3.134,95 % \u003cem\u003eCI\u003c/em\u003e : 1.012-9.698, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.048 ) were independent risk factors for moderate to severe pred-PWMHs. In pred-DWMHs, hypertension and \u0026beta;2M were also included as possible influencing factors in the multivariate logistic regression equation of pred-DWMHs, given that hypertension and \u0026beta;2M were found to be associated with overall WMHs severity in the present study, but the results showed that only age (\u003cem\u003eOR\u003c/em\u003e: 1.055, 95% \u003cem\u003eCI\u003c/em\u003e: 1.011-1.102, \u003cem\u003eP\u003c/em\u003e=0.013) was a moderate to severe independent risk factor for pred-DWMHs, and no correlation was found between \u0026beta;2M and pred-DWMHs \u003cstrong\u003e(Table 3)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e: Multifactorial Logistic Regression Analysis of different severity levels of pred-PWMHs and pred-DWMHs\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"542\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13.8634%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 32.1988%;\"\u003e\n \u003cp\u003epred-PWMHs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 32.4969%;\"\u003e\n \u003cp\u003epred-DWMH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.3043%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.3043%;\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5901%;\"\u003e\n \u003cp\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.3043%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.3043%;\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7391%;\"\u003e\n \u003cp\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.8634%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.3043%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.3043%;\"\u003e\n \u003cp\u003e1.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5901%;\"\u003e\n \u003cp\u003e(1.028~1.132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.3043%;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.3043%;\"\u003e\n \u003cp\u003e1.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7391%;\"\u003e\n \u003cp\u003e(1.011~1.102)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.8634%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.3043%;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.3043%;\"\u003e\n \u003cp\u003e3.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5901%;\"\u003e\n \u003cp\u003e(1.213~11.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.3043%;\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.3043%;\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7391%;\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.8634%;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.3043%;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.3043%;\"\u003e\n \u003cp\u003e4.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5901%;\"\u003e\n \u003cp\u003e(1.086~14.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.3043%;\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.3043%;\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7391%;\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.8634%;\"\u003e\n \u003cp\u003e\u0026beta;2M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.3043%;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.3043%;\"\u003e\n \u003cp\u003e3.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5901%;\"\u003e\n \u003cp\u003e(1.012~9.698)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.3043%;\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.3043%;\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7391%;\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the study of risk factors for WMHs, Koohi et al.\u003csup\u003e[13]\u003c/sup\u003e used a structural equation model to screen out age and hypertension as the most important independent risk factors affecting the severity of WMHs based on 41626 subjects from the British Biobank. However, in this model, the contribution of age and hypertension to WMHs was only 16 % and 10.5 %, respectively. Therefore, the etiology of WMHs is complex, and many potential causes need to be found to explain. This study also found that age and hypertension were independent risk factors for WMHs, supporting the above conclusions. In previous studies, kidney-related biomarkers such as serum creatinine, eGFR, urinary protein, and cystatin C have been found to be associated with the severity of WMHs \u003csup\u003e[13,14]\u003c/sup\u003e. This study found that eGFR, Cystatin C and \u0026beta;2M were different in different severity of WMHs, but only \u0026beta;2M level was an independent risk factor for moderate to severe WMHs. The difference between the above results may be related to the fact that the subjects were from CSVD rather than CKD patients, and it also supports that \u0026beta;2M may be a more sensitive biomarker of renal function than eGFR and cystatin C\u003csup\u003e\u0026nbsp;[6]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThis study shows that the serum \u0026beta;2M level of moderate to severe WMHs is higher than that of none or mild group, which verifies the previous research results \u003csup\u003e[9]\u003c/sup\u003e. \u0026beta;2M is a small molecule protein with a molecular weight of approximately 11.8 KDa, which is widely present on the surface of nucleated cells and is involved in the composition of major histocompatibility complex class I molecules (MHC I), which is essential for MHC I to perform its normal antigen-presenting function\u003csup\u003e[15]\u003c/sup\u003e.\u0026nbsp;In normal physiological processes, \u0026beta;2M is interpreted into the circulation with the cell division, and small molecules of \u0026beta;2M can freely pass through the glomerular filtration membrane, but 99 % is reabsorbed at the proximal convoluted tubules. Unlike serum creatinine, which is affected by muscle metabolism, the variability of serum \u0026beta;2M level is low, and its fluctuation is more sensitive than eGFR estimated based on serum creatinine\u003csup\u003e[16]\u003c/sup\u003e. Therefore, \u0026beta;2M can reflect its early predictive value in CKD and CSVD with similar pathophysiological background of small vessels. On the other hand, because free small molecule \u0026beta;2M is easy to cross the blood-brain barrier \u003csup\u003e[17]\u003c/sup\u003e，it may also be directly involved in the process of central nervous system diseases. In a cross-sectional study of 387 healthy people of different age groups, \u0026beta;2M levels increased with age and were associated with aging-related heart, kidney, and liver metabolic dysfunction \u003csup\u003e[18]\u003c/sup\u003e. In the study of aging mechanism, \u0026beta;2M was found to negatively regulate the regeneration function of hippocampal neurons with the increase of age of experimental mice, resulting in cognitive impairment. Therefore, \u0026beta;2M is also considered as one of the possible pro-aging factors\u003csup\u003e[19]\u003c/sup\u003e.\u0026nbsp;In cerebrovascular disease, the level of serum \u0026beta;2M in patients with ischemic stroke is higher than that in hemorrhagic stroke or healthy controls, which is an independent risk factor for ischemic stroke and is associated with a high risk of recurrence\u003csup\u003e[20]\u003c/sup\u003e. In conclusion, WMHs is a small vascular disease closely related to aging, and it is of great significance to understand how \u0026beta;2M participates in the production of WMHs.\u003c/p\u003e\n\u003cp\u003eThe specific mechanism by which \u0026beta;2M affects the severity of WMHs remains unclear. As mentioned above, \u0026beta;2M is elevated in patients with ischemic stroke. This phenomenon also exists in the middle cerebral artery occlusion ( MCAO ) mouse stroke model. The protein expression levels of MHC I molecule and \u0026beta;2M in the lesion side of the model were significantly higher than those in the undamaged contralateral hemisphere and the sham operation control group\u003csup\u003e[21]\u003c/sup\u003e.\u0026nbsp;Whereas, MHC I molecule is present in the plasma membrane of axons and dendrites prior to full synaptic maturation during neuronal repair or ontogeny and negatively regulate the density and normal function of synaptic connections in glutamatergic and \u0026gamma;-aminobutyric acidergic neurons\u003csup\u003e[22]\u003c/sup\u003e. In the above process, the highly expressed \u0026beta;2M binds to the MHC I heavy chain through non-covalent bonds and participates in stabilizing its configuration to exert negative biological effects \u003csup\u003e[21]\u003c/sup\u003e. This injury mechanism needs to be paid attention to in WMHs with hypoxic-ischemic injury. Chronic hemodynamic changes secondary to atherosclerosis caused by traditional risk factors such as hypertension and lipid metabolism disorders, and insufficient cerebral perfusion are also one of the reasons for increasing the burden of WMHs \u003csup\u003e[23]\u003c/sup\u003e. Studies have shown that atherosclerosis is also a chronic inflammatory vascular disease involving vascular endothelial cells and immune cells\u003csup\u003e[24]\u003c/sup\u003e. Accompanying chronic, low-grade inflammatory responses, innate and acquired immune cells are enriched in atherosclerotic plaque tissues,\u0026nbsp;where monocytes play a role in plaque progression by inducing phenotypic switching of vascular smooth muscle cells through NLRP3 inflammatory vesicle activation\u003csup\u003e[25]\u003c/sup\u003e. A recent study found that abnormal aggregation of \u0026beta;2M can activate NLRP3 inflammasome, leading to excessive secretion of IL-1\u0026beta; and IL-18, thereby promoting the progression of multiple myeloma \u003csup\u003e[26]\u003c/sup\u003e. Chen et al.\u003csup\u003e[27]\u003c/sup\u003e also found that the levels of \u0026beta;2M, NLRP3 inflammasome and pro-inflammatory factors ( IL-1\u0026beta;, IL-6 and TNF ) in ischemic brain tissue were significantly increased in the MCAO model of rats. After knocking out the \u0026beta;2M gene, the levels of NLRP3 inflammasome and pro-inflammatory factors were significantly reduced. The above studies provide some evidence that \u0026beta;2M may participate in the pathological process of WMHs as a pro-inflammatory factor by activating the NLRP3 inflammasome pathway. It should be noted that both the brain and kidney are parenchymal organs with continuous perfusion of high blood volume and have similar hemodynamic characteristics. Compared with the limited volume and mass in the human body, the brain and kidney receive about 45 % of the total cardiac output\u003csup\u003e[28,29]\u003c/sup\u003e.\u0026nbsp;Even if there is a large blood pressure fluctuation within the physiological range, the brain and renal vessels need to maintain a strong vascular tension, providing a constant perfusion pressure from the main blood vessels, perforating vessels to small blood vessels, to meet the brain \u0026apos;s vigorous energy needs and kidney filtration needs\u003csup\u003e[30]\u003c/sup\u003e.\u0026nbsp;When hypertension, diabetes, aging and other factors lead to vascular injury and decreased compliance, the brain and kidney reflect the decompensation of blood perfusion and vascular regulation in the form of small vascular disease. At this time,\u0026nbsp;\u0026beta;2M\u0026nbsp;increased due to decreased glomerular filtration function ; therefore,\u0026nbsp;\u0026beta;2M\u0026nbsp;itself is a common biomarker for small vessel disease.\u003c/p\u003e\n\u003cp\u003ePWMHs and DWMHs often coexist in the occurrence and progression of WMHs. However, studies have shown that in addition to distributional differences, these two WMHs types have different histopathological manifestations, suggesting that they are partially different in pathogenesis, with PWMHs likely to be more involved in cerebrospinal fluid leakage and inflammatory processes\u003csup\u003e[31]\u003c/sup\u003e, Chronic ischemia and hypoxia play a greater role in the formation of DWMHs\u003csup\u003e[32]\u003c/sup\u003e. As an inflammatory marker that can respond to vascular inflammation and endothelial dysfunction, Hcyt was found to be only associated with PWMHs \u003csup\u003e[12]\u003c/sup\u003e. Autosomal dominant cerebral artery disease ( CADASIL ) with subcortical infarction and leukoencephalopathy, as a hereditary cerebral small vessel disease caused by Notch3 gene mutation, although its white matter lesions also involve periventricular, its characteristic early imaging changes are WMHs in deep white matter regions such as frontal, temporal lobe and external capsule \u003csup\u003e[33]\u003c/sup\u003e. Based on the possible differences between PWMHs and DWMHs, this study preliminarily explored the relationship between \u0026beta;2M and the two respectively. The results showed that \u0026beta;2M was still associated with moderate to severe PWMHs except for age, male, and hypertension. However, in DWMHs, \u0026beta;2M and other factors did not show correlation except for age. There is no previous study on the difference in the effect of \u0026beta;2M on WMHs in different parts. It is speculated that it may be similar to Hcyt and produce effects through inflammatory processes. On the other hand, this result provides partial evidence for the existence of some pathophysiologic background differences between PWMHs and DWMHs. Regarding hypertension, a traditional risk factor clearly identified in overall WMHs, the correlation with DWMHs was not confirmed in this study, in agreement with the results of Griffanti\u003csup\u003e[34]\u003c/sup\u003e, in whose study hypertension was only associated with PWMHs and did not show a correlation with DWMHs; in addition to a possible heterogeneity of WMHs, this result needs to be confirmed by a high-quality study.\u003c/p\u003e\n\u003cp\u003eThere are still some shortcomings in this study : First, as a single-center cross-sectional study, it is impossible to provide more information to make causal inferences about the relationship between \u0026beta;2M and WMHs. Secondly, according to the difference of Fazekas score \u0026ge; 1, it is not possible to distinguish PWMHs and DWMHs patients with high quality. In this study, 45 % of the brain WMHs did not have a site advantage. Although pred-PWMHs and pred-DWMHs also preliminarily revealed the difference in the predictive value of \u0026beta;2M for different parts of WMHs, the results still need to be interpreted carefully. With the development of imaging technology, the results of this study need to be further verified on the basis of accurate evaluation of WMHs.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings suggest that \u0026beta;2M is an independent risk factor for moderate to severe overall WMHs and moderate to severe pred-PWMHs, and it may be a potential biomarker for assessing the severity of WMHs. The risk of overall WMHs severity was increased when the \u0026beta;2M\u0026ge;\u0026nbsp;value was 2.295.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFei Wang: Conceived, designed, and implemented the study; collected, analyzed, and interpreted the data; performed statistical analysis; and wrote the paper. Tingting Liu: Collected, analyzed, and interpreted the data; performed statistical analysis. Jun He and Mingwu Xia: Obtained research funding; conceived, designed, and directed the study; analyzed and interpreted the data; revised the paper. Rongfeng Wang: Obtained research funding; conceived, designed, and directed the study; analyzed and interpreted the data; performed statistical analysis; revised the thesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by a grant from the Applied Medical Research Program, Hefei Municipal Health and Wellness Committee (Hwk2022zd004).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are available from the corresponding author on reason able request. The data are not publicly available due to privacy or ethical restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involving human participants were reviewed and approved by Ethics Committee of \u0026nbsp;the Second People\u0026rsquo;s Hospital of Hefei City(NO.2022-S077). All methods were performed in accordance with the Declaration of Helsinki and the relevant guidelines and regulations. Informed consent to participate was waived by the Ethics Committee of Hefei Second People\u0026apos;s Hospital Affiliated to Bengbu Medical University due to the retrospective nature of the study design.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conficts of interest\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDas AS, Regenhardt RW, Vernooij MW, et al. Asymptomatic Cerebral Small Vessel Disease: Insights from Population-Based Studies[J]. J Stroke. 2019;21(2):121\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5853/jos.2018.03608\u003c/span\u003e\u003cspan address=\"10.5853/jos.2018.03608\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Havenon A, Smith EE, Sharma R, et al. Improvement in the Prediction of Cerebrovascular Events With White Matter Hyperintensity[J]. J Am Heart Association. 2023;12(13):e029374. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/JAHA.123.029374\u003c/span\u003e\u003cspan address=\"10.1161/JAHA.123.029374\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGouw AA, Seewann A, Van Der Flier WM, et al. Heterogeneity of small vessel disease: a systematic review of MRI and histopathology correlations[J]. J Neurol Neurosurg Psychiatry. 2011;82(2):126\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/jnnp.2009.204685\u003c/span\u003e\u003cspan address=\"10.1136/jnnp.2009.204685\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao T, Song G, Li Y, et al. Chronic kidney disease correlates with MRI findings of cerebral small vessel disease[J]. Ren Fail. 2021;43(1):255\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/0886022X.2021.1873804\u003c/span\u003e\u003cspan address=\"10.1080/0886022X.2021.1873804\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi L, Dong M, Wang XG. The Implication and Significance of Beta 2 Microglobulin: A Conservative Multifunctional Regulator[J]. Chin Med J. 2016;129(4):448\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/0366-6999.176084\u003c/span\u003e\u003cspan address=\"10.4103/0366-6999.176084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin YX, Zhang S, Xiao J, et al. Association between serum β2-microglobulin levels and the risk of all-cause and cardiovascular disease mortality in chinese patients undergoing maintenance hemodialysis[J]. BMC Nephrol. 2023;24(1):170. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12882-023-03191-5\u003c/span\u003e\u003cspan address=\"10.1186/s12882-023-03191-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu ZY, Tang F, Yang JZ, et al. The Role of Beta2-Microglobulin in Central Nervous System Disease[J]. Cell Mol Neurobiol. 2024;44(1):46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10571-024-01481-6\u003c/span\u003e\u003cspan address=\"10.1007/s10571-024-01481-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu FY, Wu W, Liu Q, et al. β2-Microglobulin is a Novel and Reliable Biomarker for Predicting Ischemic Stroke Recurrence: A Prospective Cohort Study[J]. Front Pharmacol. 2022;13:916769. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fphar.2022.916769\u003c/span\u003e\u003cspan address=\"10.3389/fphar.2022.916769\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, Leng Hl et al. Role of elevated serum β2 microglobulin on cerebral white matter damage in patients with cerebral small vessel disease[J]. J Med Student Res 2022.35(6): 612\u0026ndash;610.16571/j.cnki.1008-8199.2022.06.008\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuering M, Biessels GJ, Brodtmann A, et al. Neuroimaging standards for research into small vessel disease\u0026mdash;advances since 2013[J]. Lancet Neurol. 2023;22(7):602\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S1474-4422(23)00131-X\u003c/span\u003e\u003cspan address=\"10.1016/S1474-4422(23)00131-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFazekas F, Chawluk J, Alavi A, et al. MR Signal Abnormalities at 1.5 T in Alzheimer\u0026rsquo;s Dementia and Normal Aging[J]. AJR Am J Roentgenol. 1987;149(2):351\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2214/ajr.149.2.351\u003c/span\u003e\u003cspan address=\"10.2214/ajr.149.2.351\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee KO, Woo MH, Chung D, et al. Differential Impact of Plasma Homocysteine Levels on the Periventricular and Subcortical White Matter Hyperintensities on the Brain[J]. Front Neurol. 2019;10:1174. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fneur.2019.01174\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2019.01174\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoohi F, Harshfield EL, Markus HS. Contribution of Conventional Cardiovascular Risk Factors to Brain White Matter Hyperintensities[J]. J Am Heart Association. 2023;12(14):e030676. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/JAHA.123.030676\u003c/span\u003e\u003cspan address=\"10.1161/JAHA.123.030676\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreco F, Quarta LG, Parizel PM, et al. Relationship between chronic kidney disease and cerebral white matter hyperintensities: a systematic review[J]. Quant Imaging Med Surg. 2023;13(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21037/qims-22-707\u003c/span\u003e\u003cspan address=\"10.21037/qims-22-707\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSivanathan PC, Ooi KS, Mohammad Haniff M. Lifting the Veil: Characteristics, Clinical Significance, and Application of β-2-Microglobulin as Biomarkers and Its Detection with Biosensors[J]. ACS Biomater Sci Eng. 2022;8(8):3142\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acsbiomaterials.2c00036\u003c/span\u003e\u003cspan address=\"10.1021/acsbiomaterials.2c00036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSelvin E, Juraschek SP, Eckfeldt J, et al. Within-Person Variability in Kidney Measures[J]. Am J Kidney Dis. 2013;61(5):716\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1053/j.ajkd.2012.11.048\u003c/span\u003e\u003cspan address=\"10.1053/j.ajkd.2012.11.048\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao Y, Hong Y, Huang L, et al. β2-microglobulin functions as an endogenous NMDAR antagonist to impair synaptic function[J]. Cell. 2023;186(5):1026\u0026ndash;e103820. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2023.01.021\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2023.01.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong X, Cai R, Yang F, et al. Predictive value of plasma β2-microglobulin on human body function and senescence[J]. Eur Rev Med Pharmacol Sci. 2016;20(11):2350\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith LK, He Y, Park JS, et al. β2-microglobulin is a systemic pro-aging factor that impairs cognitive function and neurogenesis[J]. Nat Med. 2015;21(8):932\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nm.3898\u003c/span\u003e\u003cspan address=\"10.1038/nm.3898\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQun S, Hu F, Wang G, et al. Serum beta2-microglobulin levels are highly associated with the risk of acute ischemic stroke[J]. Sci Rep. 2019;9(1):6883. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-019-43370-9\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-43370-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdelson JD, Barreto G, Xu L. Neuroprotection from Stroke in the Absence of MHCI or PirB[J]. Neuron. 2012;73(6):1100\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuron.2012.01.020\u003c/span\u003e\u003cspan address=\"10.1016/j.neuron.2012.01.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlynn MW, Elmer BM, Garay PA, et al. MHCI negatively regulates synapse density during the establishment of cortical connections[J]. Nat Neurosci. 2011;14(4):442\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nn.2764\u003c/span\u003e\u003cspan address=\"10.1038/nn.2764\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBernbaum M, Menon BK, Fick G, et al. Reduced Blood Flow in Normal White Matter Predicts Development of Leukoaraiosis[J]. J Cereb Blood Flow Metabolism. 2015;35(10):1610\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/jcbfm.2015.92\u003c/span\u003e\u003cspan address=\"10.1038/jcbfm.2015.92\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoehnlein O, Libby P. Targeting inflammation in atherosclerosis \u0026mdash; from experimental insights to the clinic[J]. Nat Rev Drug Discovery. 2021;20(8):589\u0026ndash;610. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41573-021-00198-1\u003c/span\u003e\u003cspan address=\"10.1038/s41573-021-00198-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurger F, Baptista D, Roth A, et al. NLRP3 Inflammasome Activation Controls Vascular Smooth Muscle Cells Phenotypic Switch in Atherosclerosis[J]. Int J Mol Sci. 2021;23(1):340. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms23010340\u003c/span\u003e\u003cspan address=\"10.3390/ijms23010340\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHofbauer D, Mougiakakos D, Broggini L, et al. β2-microglobulin triggers NLRP3 inflammasome activation in tumor-associated macrophages to promote multiple myeloma progression[J]. Immunity. 2021;54(8):1772\u0026ndash;e17879. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.immuni.2021.07.002\u003c/span\u003e\u003cspan address=\"10.1016/j.immuni.2021.07.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen F, Liu J, Li FQ, et al. β2-Microglobulin exacerbates neuroinflammation, brain damage, and cognitive impairment after stroke in rats[J]. Neural Regeneration Res. 2023;18(3):603. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/1673-5374.350204\u003c/span\u003e\u003cspan address=\"10.4103/1673-5374.350204\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXing CY, Tarumi T, Liu J, et al. Distribution of cardiac output to the brain across the adult lifespan[J]. J Cereb Blood Flow Metabolism. 2017;37(8):2848\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0271678X16676826\u003c/span\u003e\u003cspan address=\"10.1177/0271678X16676826\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChapman CL, Johnson BD, Parker MD, et al. Kidney physiology and pathophysiology during heat stress and the modification by exercise, dehydration, heat acclimation and aging[J]. Temp (Austin). 2020;8(2):108\u0026ndash;59. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/23328940.2020.1826841\u003c/span\u003e\u003cspan address=\"10.1080/23328940.2020.1826841\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajamani K. The Cerebro-Renal System- Anatomical and Physiological Considerations[J]. J Stroke Cerebrovasc Dis. 2021;30(9):105541. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jstrokecerebrovasdis.2020.105541\u003c/span\u003e\u003cspan address=\"10.1016/j.jstrokecerebrovasdis.2020.105541\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmidt R, Schmidt H, Haybaeck J, et al. Heterogeneity in age-related white matter changes[J]. Acta Neuropathol. 2011;122(2):171\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00401-011-0851-x\u003c/span\u003e\u003cspan address=\"10.1007/s00401-011-0851-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFernando MS, Simpson JE, Matthews F, et al. White Matter Lesions in an Unselected Cohort of the Elderly: Molecular Pathology Suggests Origin From Chronic Hypoperfusion Injury[J]. Stroke. 2006;37(6):1391\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/01.STR.0000221308.94473.14\u003c/span\u003e\u003cspan address=\"10.1161/01.STR.0000221308.94473.14\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Sullivan M, Jarosz JM, Martin RJ, et al. MRI hyperintensities of the temporal lobe and external capsule in patients with[J]. Neurology. 2001;56(5):628\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1212/wnl.56.5.628\u003c/span\u003e\u003cspan address=\"10.1212/wnl.56.5.628\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGriffanti L, Jenkinson M, Suri S, et al. Classification and characterization of periventricular and deep white matter hyperintensities on MRI: A study in older adults[J]. NeuroImage. 2018;170:174\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2017.03.024\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2017.03.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cerebral Small Vessel Disease, White Matter Hyperintensities, β2-Microglobulin, Magnetic Resonance Imaging","lastPublishedDoi":"10.21203/rs.3.rs-6284537/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6284537/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: To investigate the association between serum β2-microglobulin (β2M) levels and the severity of White Matter Hyperintensities (WMHs) in patients with Cerebral Small Vessel Disease (CSVD), in addition to evaluate its predictive value for WMHs severity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We consecutively enrolled in patients with CSVD demonstrating WMHs on MRI from the Neurology Department at the Second People's Hospital of Hefei City between December 2021 and April 2024. Patient characteristics including demographic,baseline clinical, laboratory data, serum β2M levels and brain MRI features were collected. The severity of periventricular white matter hyperintensities (PWMHs) and deep white matter hyperintensities (DWMHs) was assessed using the Fazekas scale. Based on the sum of the scores from these two regions, patients were classified into a none or mild overall WMHs group (Fazekas score 0–2) and a moderate to severe overall WMHs group (Fazekas score 3–6). Patients were classified into a predominant periventricular white matter hyperintensities (pred-PWMHs) subgroup and a predominant deep white matter hyperintensities (pred-DWMHs) subgroup based on a score difference of ≥ 1 point between the two regions. Each subgroup was further divided into mild (Fazekas score 1) and moderate to severe groups (Fazekas score 2–3). Independent risk factors associated with moderate to severe overall WMHs, PWMHs and DWMHs in patients with CSVD were analyzed using univariate and multivariate logistic regression. The predictive value of β2M for moderate to severe overall WMHs was evaluated using receiver operating characteristic (ROC) curves.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 346 patients were enrolled in the study, including 183 patients with none or mild overall WMHs and 163 patients with moderate to severe overall WMHs. Univariate analysis revealed that age (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), hypertension (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), fibrinogen (\u003cem\u003eP\u003c/em\u003e = 0.019), Hcyt(\u003cem\u003eP\u003c/em\u003e = 0.005), CysC (\u003cem\u003eP\u003c/em\u003e = 0.001), Total cholesterol (\u003cem\u003eP\u003c/em\u003e = 0.025), LDL-C(\u003cem\u003eP\u003c/em\u003e = 0.012), eGFR (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) and β2M (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) were associated with the severity of overall WMHs. Multivariate logistic regression analysis identified age (\u003cem\u003eOR\u003c/em\u003e:1.050, 95%\u003cem\u003eCI\u003c/em\u003e:1.025–1.075, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001༉, hypertension (\u003cem\u003eOR\u003c/em\u003e:2.007, 95%\u003cem\u003eCI\u003c/em\u003e:1.202–3.349, \u003cem\u003eP\u003c/em\u003e = 0.008) and β2M (\u003cem\u003eOR\u003c/em\u003e:1.635, 95%\u003cem\u003eCI\u003c/em\u003e:1.154–2.317, \u003cem\u003eP\u003c/em\u003e = 0.006) as independent risk factors for moderate to severe overall WMHs. ROC curve analysis demonstrated that a β2M cut off value of 2.295 was significantly predictive of moderate to severe overall WMHs (AUC = 0.673, P \u0026lt; 0.001).In subgroup analysis, β2M was also identified as an independent risk factor for moderate to severe pred-PWMHs (\u003cem\u003eOR\u003c/em\u003e:3.134, 95%\u003cem\u003eCI\u003c/em\u003e: 1.012–9.698, \u003cem\u003eP\u003c/em\u003e = 0.048),while no association was observed with the severity of pred-DWMHs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Serum β2M levels are significantly associated with the severity of overall WMHs and pred-PWMHs,but not pred-DWMHs. Furthermore, β2M levels exhibit predictive value for moderate to severe overall WMHs.\u003c/p\u003e","manuscriptTitle":"Association and predictive value analysis of β2-microglobulin and the severity of white matter hyperintensities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 03:05:38","doi":"10.21203/rs.3.rs-6284537/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"11553ebd-0faf-4316-8c54-3239077d37ae","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-21T11:19:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 03:05:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6284537","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6284537","identity":"rs-6284537","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}