Inflammation and Cerebral Small Vessel Disease: A Systematic Review Audrey Lowa, Elijah Maka, James B. Roweb, Hugh S. Markusb, John T. O’Briena a Department of Psychiatry, University of Cambridge, United Kingdom b Department of Clinical Neurosciences, University of Cambridge, United Kingdom Corresponding Author: Professor John T. O’Brien Foundation Professor of Old Age Psychiatry Department of Psychiatry, University of Cambridge School of Clinical Medicine, Box 189, Level E4 Cambridge Biomedical Campus Cambridge, CB2 0SP, UK. E-mail address: john.obrien@medschl.cam.ac.uk (J.T. O’Brien) Word count: 6103 words Abstract: 254 words Number of Tables: 1 Number of Figures: 3 CONTENTS 1. Introduction 5 1.1. White matter hyperintensities 6 1.2. Lacunes 6 1.3. Enlarged perivascular spaces 7 1.4. Cerebral microbleeds 7 2. Methods 8 2.1. Search strategy and selection criteria 8 2.2. Inclusion and exclusion criteria 8 2.3. Data extraction 9 3. Results 10 3.1. White matter hyperintensities 11 3.2. Lacunes 12 3.3. Enlarged perivascular spaces 14 3.4. Cerebral microbleeds 14 4. Discussion 15 4.1. Blood-brain barrier and endothelial dysfunction 16 4.2. Longitudinal associations 17 4.3. Regional distribution of small vessel disease 19 4.3.1. Type 1: Hypertensive arteriopathy (arteriolosclerosis) 19 4.3.2. Type 2: Cerebral amyloid angiopathy 20 4.4. Factors influencing associations between inflammation and SVD 21 4.4.1. Diagnostic group differences 21 4.4.2. Sex differences 21 4.4.3. Cross-ethnic differences 22 4.4.4. APOE genotype 22 4.5. Evidence from animal studies 23 4.6. Therapeutic interventions 23 4.7. Future directions 24 4.7.1. Differentiation of vascular inflammation and systemic inflammation 24 4.7.2. Need for more longitudinal studies 24 4.7.3. Blood-brain barrier measurements 25 4.7.4. Need for high-resolution imaging for reliable SVD detection 25 4.7.5. Simultaneous investigation of markers 25 4.7.6. Inflammation and SVD in dementia 26 4.8. Strengths and Limitations 26 4.9. Conclusion 26 Declarations of interests 28 Authors’ contributions 28 Acknowledgements 28 References 29 ABSTRACT Inflammation is increasingly implicated as a risk factor for dementia, stroke, and small vessel disease (SVD). However, the underlying mechanisms and causative pathways remain unclear. We systematically reviewed the existing literature on the associations between markers of inflammation and SVD (i.e., white matter hyperintensities (WMH), lacunes, enlarged perivascular spaces (EPVS), cerebral microbleeds (CMB)) in cohorts of older people with good health, cerebrovascular disease, or cognitive impairment. Based on distinctions made in the literature, markers of inflammation were classified as systemic inflammation (e.g. C-reactive protein, interleukin-6, fibrinogen) or vascular inflammation/endothelial dysfunction (e.g. homocysteine, von Willebrand factor, Lp-PLA2). Evidence from 82 articles revealed relatively robust associations between SVD and markers of vascular inflammation, especially amongst stroke patients, suggesting that alterations to the endothelium and blood-brain barrier may be a driving force behind SVD. Conversely, cross-sectional findings on systemic inflammation were mixed, although longitudinal investigations demonstrated that elevated levels of systemic inflammatory markers at baseline predicted subsequent SVD severity and progression. Importantly, regional analysis revealed that systemic and vascular inflammation were differentially related to two distinct forms of SVD. Specifically, markers of vascular inflammation tended to be associated with SVD in areas typical of hypertensive arteriopathy (e.g., basal ganglia), while systemic inflammation appeared to be involved in CAA-related vascular damage (e.g., centrum semiovale). Nonetheless, there is insufficient data to establish whether inflammation is causal of, or secondary to, SVD. Findings have important implications on interventions, suggesting the potential utility of treatments targeting the brain endothelium and blood brain barrier to combat SVD and associated neurodegenerative diseases. Keywords: small vessel disease; inflammation; stroke; endothelial dysfunction; white matter hyperintensities, lacunes, enlarged perivascular spaces, cerebral microbleeds 1. Introduction Global life expectancy has increased dramatically in the last decade, bringing with it a new set of problems, such as the increasing prevalence of age-related dementia and neurodegenerative disorders (Brayne, 2007). In particular, evidence is mounting for the role of cerebral small vessel disease (SVD) and inflammation in promoting neurodegeneration and worsening its consequences (Eikelenboom et al., 2012; Vemuri et al., 2015; Wardlaw et al., 2013), although the causal associations between SVD and neurodegenerative processes are unclear. SVD is a common condition in older adults and has long been implicated with cognitive impairment, dementia, and stroke, causing up to 45% of dementia cases worldwide, and accounting for approximately one-quarter of all strokes (Pantoni, 2010). At present, the etiological basis of SVD remains controversial, although inflammation has been proposed as a candidate factor. Under many circumstances, inflammation represents a natural biological response to infections and injuries. However, an inflammatory response can have deleterious effects, causing harm to healthy tissue. One of the most widely studied markers of inflammation is C-reactive protein (CRP), a sensitive but non-specific marker of systemic inflammation (Pepys and Hirschfield, 2003). Elevated levels of CRP are a common observation in the brain and serum of patients with neurodegenerative conditions (Frank et al., 2003), and are generally associated with poorer cognitive outcomes in normal ageing (Noble et al., 2010) and neurodegenerative conditions (Schmidt et al., 2002). Another widely investigated inflammatory marker is homocysteine, which is thought to induce damage to the endothelium, and is considered a risk factor of atherosclerosis (Pang et al., 2014). These alterations to the endothelium, as measured by elevated levels of homocysteine and other vascular inflammatory markers, can result in blood-brain barrier (BBB) dysfunctions (Beard Jr et al., 2011). Due to the difficulties involved in visualising small cerebral vessels in vivo, SVD is often detected through parenchymal alterations visible on magnetic resonance imaging (MRI), rather than perturbations to the small vessels themselves. In particular, four core MRI features have been identified as markers of SVD (Huijts et al., 2013; Staals et al., 2015, 2014; Wardlaw et al., 2013), namely white matter hyperintensities (WMH), lacunes, cerebral microbleeds (CMB), and enlarged perivascular spaces (EPVS). 1.1. White matter hyperintensities WMH, also known as leukoaraiosis, represent microvascular brain lesions caused by localised changes in tissue composition. On MRI, WMH appear as patchy areas of increased intensity on T2-weighted and fluid-attenuated inversion recovery (FLAIR) sequences, and hypointense areas on T1-weighted imaging. The clinical relevance of WMH has been well-established – increased WMH burden has been found to be related to poorer cognitive outcomes (Gunning-Dixon and Raz, 2000), and heightened risk of mild cognitive impairment (MCI) and dementia (Debette and Markus, 2010). WMH are also common MRI findings amongst healthy older adults – up to 22% of healthy older adults may have moderate WMH, while 9% may have severe WMH (Hunt et al., 1989). The health of white matter is related to cardiovascular health across the lifespan (Fuhrmann et al., 2018), with WMH associated with lower diastolic blood pressure, higher systolic blood pressure, body mass, and heart rate. Even in healthy older adults, such WMH have been found to have detrimental subclinical effects on cognition (Lampe et al., 2019). 1.2. Lacunes Lacunes are small subcortical fluid-filled cavities thought to result from the occlusion of individual arteries penetrating the deep structures of the brain (e.g. basal ganglia, internal/external capsule) (Bamford and Warlow, 1988). On MRI, lacunes appear as small lesions of similar intensity to cerebrospinal fluid (CSF), i.e., hyperintense on T2-weighted images, and hypointense on FLAIR and T1-weighted images. Lacunes have been associated with greater risk of stroke, cognitive decline, and dementia (Vermeer et al., 2007, 2003). Although often assumed to be caused by acute subcortical infarcts, lacunes may also result from deep cerebral haemorrhages (Franke et al., 1991). 1.3. Enlarged perivascular spaces Virchow-Robin spaces, also known as perivascular spaces (PVS), are microscopic fluid-filled structures surrounding the small penetrating vessels of the brain. Although normally undetectable on conventional MRI, they are visible on T1- and T2-weighted images when enlarged (EPVS), appearing as small, sharply delineated structures of similar intensity to CSF. Although the mechanisms behind PVS dilation remain unclear, EPVS have been found to be associated with older age, hypertension, and stroke (Doubal et al., 2010; Zhu et al., 2010). On the other hand, however, associations between EPVS and cognition have not been consistent (Benjamin et al., 2018; Hilal et al., 2018b). PVS are particularly relevant in this investigation, given their role in leucocyte trafficking and the modulation of immune responses, both of which are implicated in the inflammatory process. 1.4. Cerebral microbleeds CMB, sometimes referred to as microhaemorrhages, are focal hemosiderin deposits resulting from tiny blood leakages from damaged arteriolar walls (Fazekas et al., 1999). They appear as small, round or ovoid foci of homogeneous signal intensity on T2*-weighted images and susceptibility-weighted imaging (SWI). CMB are linked to poorer clinical outcomes, such as cognitive dysfunction, dementia, and are predictive of mortality in older populations (Altmann-Schneider et al., 2011; Poels et al., 2012; Seo et al., 2007). Given the uncertainties surrounding the etiological mechanisms behind SVD, this systematic review aims to elucidate the associations between inflammation and SVD, as well as the factors influencing the relationship between the two pathologies. By unravelling the underlying mechanisms leading to neurodegeneration, findings could improve the prediction of disease onset, and lead to the identification of novel treatment approaches to combat SVD. 2. Methods 2.1. Search strategy and selection criteria We conducted a systematic review to identify studies investigating the relationship between markers of SVD and inflammation in dementia and stroke patients, as well as healthy older adults. The literature search was conducted on PubMed to identify relevant articles from the beginning of the database to 5 November 2018 using the following search terms: (“small vessel disease” OR “white matter hyperintensities” OR “white matter disease” OR "white matter lesions" OR “leukoaraiosis“ OR "lacunes" OR “lacunar infarcts” OR “small subcortical infarcts” OR “perivascular spaces” OR "Virchow-Robin spaces" OR “CMB” OR “microinfarcts”) AND ("neuroinflammation" OR "inflammation" OR "endothelial dysfunction" OR "interleukin-6" OR "IL-6" OR "C-reactive protein" OR "CRP" OR "tumour necrosis factor" OR "TNF-α" OR "TNFR2" OR "fibrinogen" OR "Matrix metalloproteinase 9" OR "MMP-9" OR "intercellular adhesion molecule 1" OR "ICAM-1" OR "CD40" OR "E-selectin" OR "P-selectin" OR "lipoprotein-associated phospholipase A2" OR "Lp-PLA2" OR "homocysteine" OR "vascular endothelial growth factor" OR "VEGF" OR "von Willebrand factor" OR "vWF" OR "vascular cadherin adhesion molecule-1" OR "VCAM-1" OR "TSPO" OR "microglia") AND ("healthy" OR "community" OR "dementia" OR "Alzheimer's" OR "cognitive impairment" OR "stroke"). The search was limited to include only human studies published in the English language. We further searched the reference lists of relevant articles. 2.2. Inclusion and exclusion criteria The titles and abstracts of studies identified by this search strategy were screened for relevance and eligibility for this review. Full-texts of relevant articles were read and selected according to the eligibility criteria. Only full-length research papers were considered for inclusion, and were required to include analysis studying the association between at least one MRI marker of SVD, and at least one marker of inflammation. Case studies, review papers, and studies involving multiple sclerosis, kidney failure, and patient cohorts experiencing broad or unspecified neurological discomforts were excluded. Studies were only included if their sample cohorts were made up of healthy controls, cognitively impaired participants, dementia patients, SVD, and/or stroke patients. Only samples comprising of older adults were considered, although exceptions were made for cohort studies following mid-life adults into old age. Studies where sample cohorts comprised primarily of hospitalised general neurology patients, or patients with broad neurological discomforts (e.g. vertigo, migraine, diploplia) were excluded. In the case of study duplication (i.e., two articles reporting the same findings from the same sample cohort) the more comprehensive of the two studies was retained, while the other was excluded. Articles reporting findings from the same study sample were included if they provided additional study findings that were not previously reported. 2.3. Data extraction Several key data indices were obtained from all the studies: first author, year of publication, sample size, sample characteristics (age, gender), measure of inflammation, measure of SVD, imaging characteristics (type of imaging, field strength, sequence(s) used to quantify SVD markers), study design, and results. Inflammatory markers were labelled as being measures of systemic inflammation (CRP, interleukin-6, fibrinogen, tumour necrosis factor receptor-2 (TNFR2), TNF-α, osteoprotegerin, myeloperoxidase) or vascular inflammation/endothelial dysfunction (homocysteine, ICAM-1, VCAM-1, Lp-PLA2, vascular endothelial growth factor, E-selectin, P-selectin, matrix metallopeptidase 9, neopterin, CD40). Classifications were based on distinctions made in existing literature (Ahluwalia et al., 2018; Fonseca and Izar, 2018; Goncharov et al., 2017; Hall et al., 2013; Kressel et al., 2009; Shoamanesh et al., 2015; Szmitko et al., 2003; Walker et al., 2018). Figure 1. Flowchart of study selection procedures 3. Results Our database search strategy identified 454 studies, of which 298 were excluded based on their title/abstract. The full-text of the remaining 156 articles were assessed for eligibility, removing 81 articles. The remaining 75 articles were cross-checked for repetition of findings, removing 2 articles. Searching the reference lists of relevant articles identified 9 additional studies meeting our inclusion criteria, resulting in a final sample of 82 articles published between 1994 and 2018 which were included in this systematic review. The eligibility screening process and reasons for exclusion are detailed in the flowchart provided (Fig. 1). Across the literature, the majority of studies investigated inflammation in relation to WMH (n = 66), of which 30 also included some other measures of SVD (lacunes/EPVS/CMB). The next SVD marker most frequently studied was lacunes (n = 37), followed by CMB (n = 15). Only 4 studies on EPVS were found. Principal findings of all studies are summarised in Table 1, organised according to SVD markers. 3.1. White matter hyperintensities The overall literature on inflammation and WMH was inconclusive, although findings differed based on diagnostic group. Specifically, greater WMH burden was consistently associated with vascular inflammation/endothelial dysfunction in stroke cohorts (Fig. 2), while findings remained mixed in non-stroke cohorts, and with regard to systemic inflammation in general. In terms of vascular inflammation/endothelial dysfunction, homocysteine has been the inflammatory marker most widely investigated in conjunction with WMH, and produced vastly contradictory results across literature. Notably, all 5 articles on VCAM-1 reported significant associations with WMH (Arba et al., 2018; de Leeuw et al., 2002; Huang et al., 2015; Rouhl et al., 2012; Tchalla et al., 2015). Although the literature appeared largely inconclusive overall, stratification by diagnosis group revealed that WMH and homocysteine were consistently associated amongst stroke cohorts (Fig. 2). With regard to systemic inflammation, associations between WMH and CRP are often cited as evidence of the role of inflammation in SVD. However, this systematic review showed mixed findings of cross-sectional association at best. Inconsistent findings were likewise found with regard to other markers of systemic inflammation, such as fibrinogen, and interleukin-6. To date, only one study has examined SVD in relation to neuroinflammation in the human brain, in a post-mortem investigation (Tomimoto et al., 1996). Consistent with animal studies (Farkas et al., 2004; Jalal et al., 2012), this study demonstrated increased microglial and astroglial activation in sites with WMH, providing robust evidence of a relationship between WMH and neuroinflammation in patients with ischaemic cerebrovascular disease and Alzheimer’s disease. Regional differences were also observed, whereby inflammation appeared to be preferentially related to periventricular WMH, but not deep WMH. In terms of lobar regions, higher homocysteine levels also corresponded with greater WMH in the frontal lobe (Gao et al., 2015). This finding is compatible with earlier studies demonstrating that higher CRP/homocysteine levels were significantly associated with reduced fractional anisotropy in the frontal regions (Wersching et al., 2010), and greater frontal-executive cognitive dysfunction (Sachdev, 2004; Wersching et al., 2010). Figure 2. WMH: Association with vascular inflammation (homocysteine) and systemic inflammation (CRP), stratified by diagnosis, ethnicity, and labelled by sample size. 3.2. Lacunes Evidence from 37 articles suggested that lacunar burden in healthy older adults and SVD patients was linked to higher levels of vascular inflammation/endothelial dysfunction, but not systemic inflammation. The role of vascular inflammation in lacunar infarctions was evidenced by the majority of studies on homocysteine reporting significant associations with the presence and/or number of lacunes (Fig. 3). Similarly, longitudinal investigations have also reported that higher baseline levels of homocysteine were predictive of greater prevalence and progression of lacunes. On the other hand, findings suggest the absence of a relationship between lacunes and markers of systemic inflammation, as most investigations on CRP did not identify significant associations, including 6 longitudinal investigations (Nylander et al., 2015; Satizabal et al., 2012; Schmidt et al., 2006; Staszewski et al., 2018; Van Dijk et al., 2005; Walker et al., 2017). Figure 3. Lacunes: Association with vascular inflammation (homocysteine) and systemic inflammation (CRP), stratified by diagnosis, ethnicity, and labelled by sample size. 3.3. Enlarged perivascular spaces EPVS appeared to be associated with vascular inflammation/endothelial dysfunction in stroke cohorts, and systemic inflammation in healthy cohorts. Specifically, vascular inflammatory processes appeared to be more closely tied to EPVS in the basal ganglia, while systemic inflammation was more closely associated with EPVS in the centrum semiovale. Studies on stroke/hypertension-based cohorts reported significant relationships between basal ganglia EPVS and the vascular inflammatory markers of neopterin (Rouhl et al., 2012) and von Willebrand factor (X. Wang et al., 2016), but not systemic inflammation. On the other hand, studies on community-based cohorts produced contrasting evidence, reporting instead associations between EPVS in the centrum semiovale and CRP (Aribisala et al., 2014). Notably, the relationship between CRP and EPVS were present in community-based cohorts, but not stroke cohorts. 3.4. Cerebral microbleeds Greater CMB burden was often accompanied by elevated markers of vascular inflammation/endothelial dysfunction and systemic inflammation, particularly in stroke patients. Although studies on homocysteine were largely divided, evidence of the relationship between CMB and E-selectin and vascular endothelial growth factor (VEGF) provide support of a vascular inflammatory involvement in CMB formation. In terms of systemic inflammation, there was no consensus on the relationship between CRP and CMB in healthy controls. In stroke cohorts, however, there were significant relationships between greater CMB burden and higher levels of CRP and interleukin-6. Importantly, findings regarding the relationship between CMB and inflammation differed by CMB detection protocols. Although reports were somewhat inconsistent overall, it is notable that all 4 studies employing the use of 3T SWI scans for CMB detection reported consistently robust associations (Huang et al., 2013; Lu et al., 2017; B. R. Wang et al., 2016; J. S. B. S. Zhang et al., 2016), as opposed to studies using 1.5T and/or gradient recalled-echo (GRE) scans, which are less capable of detecting CMB reliably (Cheng et al., 2013; Stehling et al., 2008). While this may suggest that the under-detection of CMB may be masking associations between inflammation and SVD, it should also be noted that 3 out of the 4 SWI studies were conducted in Alzheimer’s disease and stroke cohorts. More research should be done to examine these associations in healthy cohorts using more reliable CMB detection methods. Several studies reported region-specific associations between inflammation and deep CMB. Higher levels of CRP, TNFR2, and myeloperoxidase were associated with greater numbers of deep CMB in healthy participants, while greater homocysteine was associated with the presence of deep, but not lobar CMB. 4. Discussion This systematic review suggests that markers of vascular inflammation/endothelial dysfunction are more strongly and consistently associated with SVD, than those of systemic inflammation. Furthermore, vascular inflammation and systemic inflammation appeared to be differentially related to two distinct forms of SVD – regional differences suggest that vascular inflammatory responses play a more central role in the formation of SVD occurring in brain regions typically associated with hypertensive arteriopathy (e.g., basal ganglia), while systemic inflammation appeared to be involved in CAA-related vascular damage in lobar regions and the centrum semiovale. The existing literature suggests that vascular inflammation and endothelial dysfunction may be the driving force behind SVD via disruptions to the BBB, and can occur in the absence of systemic inflammation. Nevertheless, there is evidence that systemic inflammation is longitudinally associated with SVD progression, especially if the inflammatory response is sustained in the long term. We discuss how findings support the involvement of endothelial dysfunction and the BBB in the pathogenesis of SVD, the various factors influencing the associations between inflammation and SVD (e.g., gender, ethnicity, APOE genotype, diagnosis, duration of inflammation) , and implications on therapeutic interventions. 4.1. Blood-brain barrier and endothelial dysfunction Findings support the position that BBB dysfunction is involved in the pathogenesis of SVD, as a downstream result of endothelial dysfunction – this process appeared particularly relevant in the development of CMB and EPVS. The functions of the BBB include restricting the entry of pathogens and immune cells into the brain. However, given that the BBB is composed of endothelial cells, vascular inflammation and disruptions to endothelial function can cause the BBB to break down, increasing its permeability and allowing the entry of potentially harmful toxins and immune cells into the brain (Beard Jr et al., 2011). In turn, this has been proposed to cause changes to the small vessel structures, parenchyma, and PVS, resulting in SVD (Wardlaw, 2010). In general, findings are interpreted to suggest that endothelial dysfunction and increased BBB may contribute to the development of SVD, implicating EPVS in particular. EPVS itself is considered a marker of BBB dysfunction (Wardlaw et al., 2009), as well as a marker of neuroinflammation (Wuerfel et al., 2008). Normally, PVS function as a conduit for the drainage of interstitial fluids to the ventricles (Abbott, 2004). Being a direct pathway of interstitial drainage, however, it is also vulnerable as a gateway for harmful foreign antigens (e.g. toxins) to enter the brain. There is some evidence to suggest that vascular inflammatory processes may be involved in the dilation of PVS, as evidenced by associations between greater EPVS burden with higher levels of neopterin and lower levels of von Willebrand factor expression in plasma. Both of these are considered markers of endothelial dysfunction – (1) neopterin, a product of activated macrophages/monocytes, reflects the degree of oxidative stress induced by immune system activation (Murr et al., 2002), while (2) the von Willebrand factor is a marker of endothelial cell damage and a regulator of BBB permeability (Suidan et al., 2013). Taken together, findings demonstrate a close coupling between EPVS, endothelial dysfunction, and BBB permeability. However, due to the cross-sectional designs of the studies, it remains unclear as to whether PVS dilation is a cause, effect, or secondary process of endothelial dysfunction and increased BBB permeability. It is noteworthy that two important markers of endothelial dysfunction, E-selectin and VEGF, were found to be exclusively associated with CMB, but not the other markers of SVD (i.e., WMH, lacunes, EPVS). Soluble E-selectin is shed from damaged endothelial cells, and due to its exclusively endothelial source, it is considered to be one of the most specific measures of endothelial damage (Deanfield et al., 2007). As such, the associations demonstrated between E-selectin and the presence (Huang et al., 2013) and number (Rouhl et al., 2012) of CMB provides strong support that endothelial dysfunction places a key role in the development of CMB. As a result of the increased BBB permeability brought about by endothelial dysfunction, CMB may result from ‘leakage’ of red blood cells into the parenchyma (Nachman and Rafii, 2008), as opposed to ruptured vessels. This is further supported by evidence demonstrating associations between CMB and VEGF, a potent inducer of vascular leakage, in Alzheimer’s disease (J. S. B. S. Zhang et al., 2016) and stroke patients (Dassan et al., 2012). Somewhat surprisingly, almost all investigations on other SVD markers (i.e. WMH, lacunes, EPVS) did not find any significant associations with E-selectin or VEGF. The largely exclusive associations of CMB with E-selectin and VEGF suggests a disproportionately central role of endothelial dysfunction in CMB formation, compared to other SVD markers. However, given the limited literature, more investigations should be done to investigate the role of E-selectin in WMH, lacunes, and EPVS. Overall, although the relationship between inflammation and BBB dysfunction has been well-documented, the direction of causality remains a topic of debate. Several studies have found evidence of BBB damage preceding inflammation (Minagar et al., 2006; Tonra et al., 2001), suggesting that increased BBB permeability exposes the brain to harmful toxins and immune cells, thereby damaging the vessel walls and parenchyma of the brain, while others believe that inflammation is responsible for damages to the BBB (Varatharaj and Galea, 2017). Longitudinal investigations and more direct measures of BBB integrity are needed to elucidate the involvement of the BBB in SVD. 4.2. Longitudinal associations Cross-sectionally, the literature pertaining to associations between SVD and systemic inflammation has been inconsistent. However, longitudinal investigations demonstrated that higher levels of systemic inflammation predicted WMH progression, although it was unrelated to lacune progression. Importantly, there was evidence to suggest that the prolongation of systemic inflammation may be a key propagator of subsequent SVD progression. Findings from the Atherosclerosis Risk in Communities study found that sustained elevation of CRP levels across midlife increased the risk of SVD two decades later (Walker et al., 2018). Notably, participants who experienced high levels of CRP at baseline, followed by low CRP in later years were not at greater risk of developing WMH, which was in agreement with previous evidence that reduction in CRP levels in healthy older adults predicted better white matter microstructure, as measured by fractional anisotropy (Bettcher et al., 2015). It is plausible that short-term elevation of systemic inflammation may not be sufficient to induce a vascular inflammatory cascade leading to SVD, but when prolonged, could promote the development of SVD downstream. The significance of the prolongation of inflammation may in part explain the inconsistent findings obtained in cross-sectional studies, highlighting the inadequacy of single-time measurements. Somewhat contrasting results were obtained with regard to longitudinal associations between vascular inflammation/endothelial dysfunction and SVD. While heightened baseline levels of vascular inflammation predicted lacune progression, findings were inconclusive in relation to WMH progression. The differential involvement of the two forms of inflammation in WMH and lacunes suggests that WMH may be linked to elevations in non-specific inflammatory markers, while lacunes may stem from focal damage to the endothelium. Although current literature is yet unable to establish whether inflammation is causal of, or merely secondary to SVD, basic research studies suggest that inflammation/endothelial dysfunction are responsible, at least in part, in the pathogenesis of SVD. Recently, a single-nucleotide polymorphism in ATP11B has been identified in human SVD patients and was found to be associated with WMH. Importantly, the mutation of this gene is rats has been shown to lead to endothelial dysfunction, suggesting the involvement of inflammation/endothelial dysfunction in SVD. Nevertheless, this has yet to be established in humans. To address this, future studies would benefit from repeated measurements of inflammation alongside SVD progression, and the use of Mendelian randomisation to study the effects of genetic variants associated with inflammation on SVD (e.g., Rutten-Jacobs et al., 2016). 4.3. Regional distribution of small vessel disease The markers recognised to represent vascular inflammation and systemic inflammation seemed to be differentially associated with two distinct types of SVD, namely hypertensive arteriopathy and cerebral amyloid angiopathy (CAA), as classified by Pantoni’s etiological classification of Type 1 and Type 2 SVD (Pantoni, 2010). Inflammation, especially vascular inflammation, appeared to be preferentially associated with SVD in the periventricular area, basal ganglia, and brainstem, which are linked to hypertensive arteriopathy. Conversely, systemic inflammation, but not vascular inflammation, seemed to be involved in cortical regions, which are commonly associated with CAA. The differential vulnerability to vascular damage may be attributed to features of the cerebrovascular network making certain regions more vulnerable to endothelial dysfunction. 4.3.1. Type 1: Hypertensive arteriopathy (arteriolosclerosis) Several studies reported stronger associations between inflammation and SVD occurring in regions characteristic of hypertensive arteriopathy. This includes lesions occurring in areas supplied by deep perforating arteries, such as WMH in the periventricular region (as opposed to deep WMH), deep CMB (as opposed to lobar CMB), and EPVS occurring in the basal ganglia (as opposed to EPVS in the centrum semiovale). Anatomically, these regions are supplied by long arterioles and arteries which are susceptible to luminal narrowing resulting from atherosclerosis, especially in hypertension and diabetes mellitus. These long arteries are susceptible to twisting/looping, which can be exacerbated by hypertension, typically affecting the small perforating end-arteries. As such, lesions occurring in these regions are often attributed to hypertensive pathology, including lacunes (Kario et al., 2001), CMB (Greenberg et al., 2009), and EPVS (Charidimou et al., 2013). Given the detrimental effects of hypertension on BBB integrity (Biancardi et al., 2014), the involvement of endothelial activation in SVD positions it as a possible mediator explaining the mechanisms leading from hypertension to increased risk of SVD, especially in regions supplied by deep perforating arteries. However, the association of SVD with hypertension is qualified by the opposing effects of diastolic and systolic pressure (Fuhrmann et al., 2018), suggesting that pulse pressure may also be relevant rather than just hypertension. 4.3.2. Type 2: Cerebral amyloid angiopathy Findings from this systematic review offer new insights into the mechanisms behind CAA-related SVD, implicating systemic inflammation as a contributor to CAA-related damage. An examination of regional differences found that systemic inflammation was preferentially associated with vascular damage in regions commonly linked to CAA pathology – these are lobar regions and areas supplied by cortical and leptomeningeal vessels, which are commonly affected by CAA. For instance, systemic inflammation was associated with EPVS occurring specifically in the centrum semiovale, a pattern often observed in Alzheimer’s disease and CAA. This is in line with prior research suggesting that EPVS in the centrum semiovale, but not in the basal ganglia, may be attributed to systemic inflammation (Miyata et al., 2017). CAA results from the deposition of β-amyloid within the walls of cortical and leptomeningeal blood vessels, and preferentially affects lobar regions. This accumulation of β-amyloid plaques in the cerebral cortex activate the immune response, inducing the activation of microglia and cytokine production (Meyer-Luehmann et al., 2008), contributing to downstream damage to the parenchyma and vessels in the brain through the release of neurotoxic factors (Lucin and Wyss-Coray, 2009), perhaps explaining the findings observed. Other SVD markers occurring in this area have also been associated with CAA, including lobar CMB (Greenberg et al., 2009; Vernooij et al., 2008). While studies in this review have not reported any exclusive associations between systemic inflammation and lobar CMB, recent mouse models have demonstrated bidirectional associations between the two, whereby CMB may be induced by systemic inflammation, and also heighten levels of inflammation (Ahn et al., 2018; Sumbria et al., 2018). However, more studies are required to reach a definitive conclusion in human populations. 4.4. Factors influencing associations between inflammation and SVD 4.4.1. Diagnostic group differences The association between inflammation and SVD were largely confined to patient cohorts, but was not observed in healthy older adults. For instance, the relationship between WMH and homocysteine was inconclusive within healthy older adults, although stratification by diagnosis group revealed that majority of studies on stroke and Alzheimer’s disease cohorts demonstrated significant associations (Fig. 2). Similarly, the relationship between homocysteine and lacunes was not conclusive in healthy older adults, while all four studies in SVD cohorts reported significant associations (Fig. 3). Evidence of associations between inflammation and CMB and EPVS were also more robust in stroke cohorts. The lack of a relationship in healthy cohorts may perhaps be attributed to low or minimal levels of pathology. 4.4.2. Sex differences Levels of inflammation and cerebrovascular damage differed significantly between males and females, although there was insufficient evidence to determine whether sex influenced the association between SVD and inflammation. In general, studies in this review reported higher levels of systemic (CRP, NLR) and vascular inflammation (homocysteine) in males, compared to females (L. Feng et al., 2013; Hassan et al., 2004; Hogervorst et al., 2002; Ma et al., 2010; Matsui et al., 2001; Nam et al., 2017; Wright et al., 2005). In relation to cerebrovascular damage, however, females displayed greater WMH burden than males (Cloonan et al., 2015; Hogervorst et al., 2002; Sachdev, 2004; Wright et al., 2005). However, few studies investigated the effect of sex on the association between SVD and inflammation, reporting contrasting findings. Some studies found exclusive associations between SVD and inflammation in females, but not males (Hogervorst et al., 2002; Riba-Llena et al., 2014), and vice versa (Sachdev, 2004), while others found that the relationship between SVD and inflammation was not influenced by gender (Aono et al., 2007; Khan et al., 2008; Wright et al., 2009). On top of distinct sex differences in brain structure and function (Cosgrove et al., 2007), males and females also differ in terms of cerebrovascular health (Gallart-Palau et al., 2016; Sachdev et al., 2009) and the immune response (Khera et al., 2005; Oertelt-Prigione, 2012). By examining how sex influences the association between the two processes, researchers may better elucidate the underlying mechanisms involved and identify key contributing factors, e.g. hormonal changes (Hogervorst et al., 2002; Tallova et al., 1999). 4.4.3. Cross-ethnic differences Ethnic differences appeared to influence the relationship between inflammation and SVD. In healthy controls, associations between systemic inflammation and WMH were commonly reported in Caucasian cohorts, but largely absent in Asian cohorts (Fig. 2). With regard to lacunes, however, almost all studies conducted in Caucasian cohorts reported an absence of association between lacunes and CRP, while findings were mixed in Asian cohorts (Fig. 3). Comparing blacks and whites, the significance of ethnicity was inconclusive. While some have found that blacks display stronger associations between inflammation and SVD (Khan et al., 2008; Khera et al., 2005; Walker et al., 2017), contrasting evidence exists (Fornage et al., 2008). 4.4.4. APOE genotype There was some evidence to suggest that APOE genotype moderated the association between inflammation and SVD. Significant associations in APOE ε2 and APOE ε4 carriers, but not non-carriers, suggests a heightened sensitivity to abnormal inflammatory levels amongst those in possession of these alleles (Romero et al., 2012; Walker et al., 2017). This may be explained by the increased propensity of APOE ε2 and APOE ε4 to promote inflammation, notably through distinct processes. While APOE ε2 heightens inflammation through defective receptor binding and delayed lipid clearance (Kuhel et al., 2013), APOE ε4 has been found to increase inflammation by accelerating endoplasmic reticulum stress and reducing macrophage function (Cash et al., 2012). Moreover, this may be further accentuated by the tendency for APOE ε4 carriers to produce a stronger neuroinflammatory response to peripheral systemic inflammation (Lynch et al., 2003; Ophir et al., 2005). 4.5. Evidence from animal studies While existing human studies have been unable to ascertain the direction of causality between inflammation and SVD, evidence from animal studies may help bridge the gap between basic and clinical research, suggesting that SVD may be caused by inflammation. Investigations based on rat models have generally reported robust associations between inflammation and SVD (e.g., Jalal et al., 2012; Kaiser et al., 2014; Rajani et al., 2018; Rajani and Williams, 2017), with histological studies showing activated microglia and increased expression of vascular inflammatory markers (e.g. MMP-9, TNF-α) within regions of white matter lesions (Farkas et al., 2004; Jalal et al., 2012). Animal studies have also shed light on the direction of causality, providing strong support that inflammation and endothelial dysfunction precedes, and causes, SVD (Jalal et al., 2015; Kaiser et al., 2014; Rajani et al., 2018). This is demonstrated especially by the reversal of white matter damage following the use of drugs targeting inflammation and the endothelium, as well as the identification of a single-nucleotide polymorphism of a gene in SVD patients – a gene shown to be responsible for endothelial dysfunction in rats when mutated (Jalal et al., 2015; Rajani et al., 2018). 4.6. Therapeutic interventions Although findings suggest the potential of anti-inflammatory drugs in combating SVD, there is weak evidence to support their efficacy in preventing SVD and related neurodegeneration (Bath and Wardlaw, 2015; Meyer et al., 2019; Mok and Kim, 2015; Yoon et al., 2017). While further research is currently being undertaken to assess the efficacy of anti-inflammatory drugs in SVD (Bath and Wardlaw, 2015), the lack of success thus far suggests that such drugs might be treating the symptom of inflammation, but not its underlying cause. On the other hand, interventions targeting the endothelium have shown promising results in rat models, whereby the stabilisation of endothelial cells was found to reverse white matter abnormalities (Rajani et al., 2018). Combined with evidence from this systematic review, this highlights the need to further assess the therapeutic efficacy of interventions targeting the endothelium and blood brain barrier in humans. 4.7. Future directions 4.7.1. Differentiation of vascular inflammation and systemic inflammation At present, scientists use the term ‘inflammation’ to describe both systemic and vascular inflammation. However, the variability in the associations between SVD and the two forms of inflammation suggests distinct roles and vascular outcomes in SVD, despite the fact that they can occur simultaneously. In acknowledging their differences, future research should aim to characterise and distinguish between the two inflammatory responses. 4.7.2. Need for more longitudinal studies Majority of the studies identified employed cross-sectional designs. Of the limited cohort studies, all but 2 studies measured inflammation at only one time point. Furthermore, all studies examined inflammation as a predictor of SVD, while none examined the opposite direction of causality. These limitations hinder our ability to draw reliable conclusions on the directionality of effects, or account for fluctuations in inflammation levels. This is particularly important considering the importance of prolonged systemic inflammation in propagating SVD. 4.7.3. Blood-brain barrier measurements Although the relationship between inflammation and BBB dysfunction has been well-established, the direction of causality remains a topic of debate. Longitudinal investigations, and the use of more direct measures of BBB integrity (e.g. dynamic contrast-enhanced MRI) are required to elucidate the involvement of the BBB in SVD. 4.7.4. Need for high-resolution imaging for reliable SVD detection The MRI parameters employed for the detection of SVD markers should also be considered, especially in relation to CMB detection, which most studies performed using T2*-weighted GRE scans on 1.5T MRI. However, CMB detection is known to vary dramatically depending on the field strength and scan sequence adopted – SWI scans detect 25% more CMB than GRE scans (Cheng et al., 2013), while 3T scans detect 30% more CMB than 1.5T scans (Stehling et al., 2008). As such, further research employing more reliable and consistent CMB detection is required to ensure the accurate estimation of SVD severity. 4.7.5. Simultaneous investigation of markers To distil the independent associations involved, multiple markers of inflammation and SVD should be investigate simultaneously. A number of studies limited their analysis to single markers of inflammation, which is problematic due to the strong relationships between inflammatory markers. Furthermore, in instances whereby multiple inflammatory markers were examined simultaneously, investigations often focused on either vascular or systemic inflammation, but rarely both. The same can be said about SVD markers - out of the 82 articles identified, only 2 articles have investigated all four SVD markers in tandem (Hilal et al., 2018a; Rouhl et al., 2012). 4.7.6. Inflammation and SVD in dementia Despite increasing recognition of the role of both inflammation and SVD in dementia, only four studies on dementia cohorts were identified, although an abundance of research has been conducted in healthy adults and other disorders such as multiple sclerosis. Further investigations on the associations of inflammation and SVD in dementia will be valuable in elucidating the underlying mechanisms of dementia. 4.8. Strengths and Limitations Publication bias is a problem inherent in academic literature and, by extension, systematic reviews. This systematic review was able to overcome some extent of publication bias, by virtue of the simultaneous analysis of multiple markers in many studies, reporting non-significant findings alongside significant results. In this review, we classified the various inflammatory markers into systemic and vascular inflammation based on distinctions emerging in recent research (Hall et al., 2013; Kressel et al., 2009; Shoamanesh et al., 2015). However, the various biological markers are recognised to be closely interconnected, and overlaps may exist. Further research to make distinctions between the two forms of inflammation are required for more definite assignment of inflammation type. Finally, we examined the effect of sample sizes on previous findings. However, sample size was not a major determinant of a study’s reported significance/non-significance (Fig. 2-3). 4.9. Conclusion Biological markers of vascular inflammation/endothelial dysfunction appear to be involved in the pathogenesis of SVD in stroke patients, and may be linked to underlying hypertensive arteriopathology. While cross-sectional associations with systemic inflammation have been inconclusive, heightened systemic inflammatory responses appeared to be capable of promoting the subsequent development of SVD, especially if prolonged. Combined with reports from emerging animal studies, these findings have important implications for therapeutic interventions, suggesting the potential of treatments targeting the endothelium and blood brain barrier to combat SVD, and by extension, SVD-related neurodegeneration. Declarations of interests None Authors’ contributions Audrey Low conducted the literature searches and wrote the paper. Elijah Mak reviewed the drafts, and contributed to the writing of the paper. James Rowe, Hugh Markus and John O’Brien reviewed the data and manuscript, provided critical feedback, and made revisions to the manuscript. Acknowledgements This study is supported by the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) Dementia and Neurodegeneration Theme (Grant Reference Number 146281). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. James Rowe is supported by the Wellcome Trust (103838). Hugh Markus and John O’Brien are supported by NIHR Senior Investigator awards. 39 References Abbott, N.J., 2004. Evidence for bulk flow of brain interstitial fluid: significance for physiology and pathology. Neurochem. Int. 45, 545–552. https://doi.org/10.1016/j.neuint.2003.11.006 Ahluwalia, A., Misto, A., Vozzi, F., Magliaro, C., Mattei, G., Marescotti, M.C., Avogaro, A., Iori, E., 2018. Systemic and vascular inflammation in an in-vitro model of central obesity. 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Homocysteine levels and lacunar brain infarcts in elderly women: The prospective population study of women in Gothenburg. J. Am. Geriatr. Soc. 56, 1087–1091. https://doi.org/10.1111/j.1532-5415.2008.01724.x Table 1. Summary of studies Authors (Year) Sample size and patient cohort Sample characteristics Measure of inflammation Measure of SVD SVD imaging; field strength (FS) and sequence (S) Study design Results White Matter Hyperintensities Breteler et al. (1994) 111 community-based subjects Rotterdam Study Age range: 65-84 Females: 59.5% Systemic: Fibrinogen WMH (Semi-quantitative) MRI FS: 1.5T S: T2 Cross-sectional Systemic: 1. Presence of WMH a/w higher fibrinogen levels Longstreth et al. (1996) 3,301 community-based subjects Cardiovascular Health Study Age: 65 and older Females: nr Systemic: Fibrinogen WMH (Semi-quantitative) MRI FS: 1.5T S: PD, T2 Cross-sectional Systemic: 1. WMH severity not a/w fibrinogen levels Tomimoto et al. (1996)# Post-mortem brains from 14 ischaemic CVD, 12 AD, 6 healthy controls Healthy controls Age: 76.5 ± 2.8 Females: 16.7% CVD patients Age: 79.4 ± 2.5 Females: 50.0% AD patients Age: 81.5 ± 2.2 Females: 75.0% Systemic: Fibrinogen Microglial activation: HLA-DR Astroglia: GFAP WMH (Semi-quantitative) CT/MRI FS: nr S: nr Post-mortem study Systemic: 1. Presence of WMH a/w greater fibrinogen levels Microglia activation: 2. In CVD and AD, groups with WMH presented with more microglial activation Astroglia: 3. In CVD and AD, groups with WMH presented with greater GFAP de Leeuw et al. (2002) 29 stroke patients Rotterdam Scan Study Age: 62.2 ± 12.3 Females: 13.8% Vascular: ICAM-1, VCAM-1, E-selectin, P-selectin WMH (Semi-quantitative) MRI FS: 1.5T S: PD, T2 Cross-sectional Vascular: 1. Severe PVH, but not DWMH a/w higher P-selectin and VCAM-1 levels 2. WMH not a/w ICAM-1 or E-selectin levels Hogervorst et al. (2002) 137 AD patients, 277 healthy controls Oxford Project to Investigate Memory and Ageing (OPTIMA) Study Healthy controls Age: 73.3 ± 7.7 Females: 50% AD patients Age: 73.9 ± 9.0 Females: 60% Vascular: Homocysteine WMH (Semi-quantitative) CT Cross-sectional Vascular: 1. Moderate to severe WMH a/w higher homocysteine levels 2. Homocysteine levels more strongly associated with DWMH than PVH Martí-Fàbregas et al. (2002) 28 patients (12 first-ever lacunar infarction, 16 Binswanger disease) Age: 71.0 ± 8.6 Females: 25.0% Systemic: Fibrinogen WMH (Semi-quantitative) MRI FS: 1.5T S: PD, T2 Cross-sectional Systemic: 1. Greater WMH a/w higher fibrinogen levels Vermeer et al. (2002)b 1,077 healthy subjects Rotterdam Scan Study Age: 72.2 ± 7.4 Females: 51.5% Vascular: Homocysteine WMH (PVH – Semi-quantitative; DWMH – Quantitative) MRI FS: 1.5T S: PD, T2 Cross-sectional Vascular: 1. Presence of severe WMH a/w higher homocysteine levels 2. Severity of PVH and DWMH a/w higher homocysteine levels Hassan et al. (2003)b 110 lacunar stroke patients, 50 community controls Healthy controls Age: 66.5 ± 9.7 Females: 46.0% Lacunar stroke patients Age: 67.2 ± 10.2 Females: 35.5% Vascular: ICAM-1, TM WMH (Semi-quantitative) CT/MRI FS: nr S: T2/CT Cross-sectional Vascular: 1. Presence of WMH a/w elevated TM and ICAM-1 levels 2. Severity of WMH a/w TM but not ICAM-1 levels Dufouil et al. (2003) 1,241 community-based participants Epidemiology of Vascular Ageing (EVA) study Age: 67.0 ± 3.0 Females: 58.6% Vascular: Homocysteine WMH (Semi-quantitative) MRI FS: 1T S: PD, T2 Cohort study Vascular: 1. Cross-sectionally, WMH severity not a/w homocysteine levels Hassan et al. (2004)b 172 SVD patients and 172 community controls Healthy controls Age: 66.3 ± 10.2 Females: 41.9% SVD patients Age: 67.1 ± 10.3 Females: 40.7% Vascular: Homocysteine WMH (Semi-quantitative) MRI FS: nr S: T2 Cross-sectional Vascular: 1. Higher homocysteine observed in patients with WMH, compared to both patients with isolated lacunar infarct and controls Longstreth et al. (2004)b 622 non-stroke, non-TIA participants Cardiovascular Health Study Age: 65 and above (61.3% above 75) Females: 46.6% Vascular: Homocysteine WMH (Semi-quantitative) MRI FS: nr S: T1, T2 Cross-sectional Vascular: 1. WMH not a/w homocysteine levels Sachdev (2004) Sydney Stroke Study 131 ischaemic stroke patients, 81 healthy subjects PATH Study 385 community-dwelling subjects Sydney Stroke Study Age range: 55-87 Females: nr PATH Study Age range: 60-64 Male Age: 62.6 ± 1.4 Female Age: 62.6 ± 1.5 Females: 49.1% Vascular: Homocysteine Sydney Stroke Study WMH (Semi-quantitative) PATH study WMH (Quantitative) MRI FS: 1.5T S: FLAIR Cross-sectional Sydney Stroke Study Vascular: 1. DWMH not a/w homocysteine after correcting for age PATH Study Vascular: 2. Greater DWMH volume a/w higher homocysteine level Markus et al. (2005) 267 community-based subjects Austrian Stroke Prevention Study Age: 60.0 ± 6.0 Females: 49.4% Vascular: ICAM-1 WMH (Quantitative + Semi-quantitative) MRI FS: 1.5T S: PD, T2 Cohort study Vascular: 1. WMH progression a/w ICAM-1 Van Dijk et al. (2005)b 1,033 dementia-free, community-based sample Rotterdam Scan Study Age: 72 ± 7 Females: 51% Systemic: CRP WMH (Semi-quantitative) MRI FS: 1.5T S: PD, T2 Cohort study Systemic: 1. Cross-sectionally, presence and greater severity of WMH (both PVH and DWMH) a/w higher CRP levels 2. Greater progression of PVH and DWMH a/w higher CRP levels Wright et al. (2005) 259 community-based stroke-free participants Northern Manhattan Study (NOMAS) Mean age: 64.8 Females: 55.2% Vascular: Homocysteine WMH (Quantitative) MRI FS: 1.5T S: FLAIR Cross-sectional Vascular: 1. Greater WMH volume a/w higher homocysteine levels Gunstad et al. (2006) 37 cardiovascular disease patients (subset of 128) Age: 69.2 ± 7.6 Females: 41% Systemic: CRP Vascular: Homocysteine WMH (Quantitative) MRI FS: 1.5T S: FLAIR Cross-sectional Systemic: 1. WMH not a/w CRP levels Vascular: 2. WMH not a/w homocysteine levels Naka et al. (2006)d# 102 stroke patients Age: 69.5 ± 10.3 Females: 47.1% Vascular: Homocysteine WMH (Semi-quantitative) MRI FS: 1T S: T2 Cross-sectional Vascular: 1. Presence of advanced WMH a/w higher homocysteine levels Schmidt et al. (2006)b 505 community-based participants (subset of 700) Austrian Stroke Prevention Study Age: 69.9 ± 7.7 Females: 53.7% Systemic: CRP WMH (Quantitative + Semi-quantitative) MRI FS: 1.5T S: nr Cohort study Systemic: 1. Severity or progression of WMH not a/w CRP levels Wong et al. (2006)b# 57 SVD-related stroke patients Non-Hyperhomocysteinemia Age: 68.3 ± 12.2 Females: 44.2% Hyperhomocysteinemia Age: 73.0 ± 6.8 Females: 64.3% Vascular: Homocysteine WMH (Quantitative) MRI FS: 1.5T S: T2 Cross-sectional Vascular: 1. Greater WMH a/w higher homocysteine levels Aono et al. (2007)b# 958 community-based participants The Ohasama Study Age: 66.0 ± 5.7 Females: 68% Systemic: Fibrinogen WMH (Semi-quantitative) MRI FS: 0.5T S: T2 Cross-sectional Systemic: 1. Presence of WMH a/w higher fibrinogen levels Jefferson et al. (2007) 1,926 dementia-free, stroke-free, community-based sample Framingham Heart Study Mean age: 60 ± 9 Females: 53.6% Systemic: IL-6, TNFα, TNFR2, MCP-1, MPO, OPG Vascular: CD40, ICAM-1 P-selectin WMH (Quantitative) MRI FS: 1T S: T2 Cross-sectional 1. None of the inflammatory markers a/w WMH Fornage et al. (2008) 3,644 community-based participants (3073 whites; 571 blacks) Cardiovascular Health Study Whites Age: 75.4 ± 5.0 Females: 57.5% Blacks Age: 73.9 ± 5.7 Females: 62.7% Systemic: IL-6, CRP WMH (Semi-quantitative) MRI FS: 1.5T S: T2 Cross-sectional Systemic: 1. Presence of moderate to severe WMH a/w greater IL-6 levels in white and blacks, and with greater CRP levels in whites, but not blacks Khan et al. (2008) 457 black stroke patients, 179 black healthy controls South London Ethnicity and Stroke Study Healthy controls Age: 65.4 ± 7.4 Females: 38.0% Stroke patients Age: 65.4 ± 12.2 Females: 44.0% Vascular: Homocysteine WMH (Semi-quantitative) CT/MRI FS: nr S: nr Cross-sectional Vascular: 1. WMH severity a/w higher homocysteine levels Seshadri et al. (2008)b 1,965 healthy older adults Framingham Offspring Study Age: 62 ± 9 Females: 53.4% Vascular: Homocysteine WMH (Quantitative) MRI FS: 1T S: T2 Cohort study Vascular: 1. Presence of extensive WMH at follow-up not a/w baseline or follow-up homocysteine levels Wada et al. (2008)b# 689 community-based elderly Without lacunes n = 493 Age: 67.7 ± 5.0 Females: 55.2% With lacunes n = 196 Age: 69.9 ± 3.6 Females: 57.1% Systemic: CRP WMH (Semi-quantitative) MRI FS: 0.3T/ 0.5T S: FLAIR Cross-sectional Systemic: 1. WMH severity not a/w CRP levels in multivariate analysis Zylberstein et al. (2008)b 277 women from community sample Mean age at CT scan: 73.8 Females: 100% Vascular: Homocysteine WMH (Semi-quantitative) CT Cohort study Vascular: 1. Mid-life homocysteine levels not a/w later-life WMH Baune et al. (2009)b 268 community participants Memory and Morbility in Augsburg Elderly (MEMO) Study Mean age: 72.3 Females: 47.8% Systemic: IL-1β, IL-6, IL-8, IL-10, IL-12, TNF-α WMH (Quantitative) MRI FS: 1.5T S: nr Cross-sectional Systemic: 1. None of the inflammatory markers a/w WMH Kearney-Schwartz et al. (2009) 198 hypertensive patients with subjective cognitive impairment Age: 69.3 ± 6.2 Females: 53.0% Vascular: vWF WMH (Semi-quantitative) MRI FS: 1.5T S: T2 images Cross-sectional Vascular 1. Greater WMH severity a/w greater vWF Wright et al. (2009) 527 stroke-free participants Northern Manhattan Study (NOMAS) Mean age: 71.3 Females: 58% Systemic: CRP Vascular: Lp-PLA2, MPO WMH (Quantitative) MRI FS: 1.5T S: nr Cross-sectional Systemic: 1. Greater WMH volume a/w higher CRP levels 2. Adjusting for all three biomarkers simultaneously, WMHV association with higher Lp-PLA2 and MPO remained significant, while the association with CRP disappeared. Knottnerus et al. (2010) 149 lacunar stroke patients Asymptomatic lacunar infarct without extensive WMH Age: 60.4 ± 13.0 Female: 30.2% Isolated lacunar infarct Age: 58.9 ± 10.0 Female: 39.5% With extensive WMH Age: 68.2 ± 9.3 Female: 43.4% Vascular: VWF, PAI-1, TM WMH (Semi-quantitative) MRI FS: 1.5T/ 3T S: FLAIR Cross-sectional Vascular: 1. Presence of extensive WMH a/w lower PA1-1 but was not a/w vWF and TM Ma et al. (2010)# 377 acute ischaemic stroke patients, 106 patients with transient ischaemic attack Acute ischaemic stroke Age: 60.5 ± 11.6 Females: 24.7% Transient ischaemic attack Age: 61.3 ± 11.7 Females: 30.2% Vascular: Homocysteine WMH (Semi-quantitative) CT/MRI FS: nr S: nr Cross-sectional Vascular: 1. Greater WMH severity a/w elevated homocysteine level Oberheiden et al. (2010) 24 SVD patients, 10 healthy controls Healthy controls Age: 64.6 ± 12.9 Females: 40.0% SVD patients Age: 77.3 ± 5.8 Females: 58.3% Systemic: IL-6, IL-7 Vascular: CD40 WMH (Semi-quantitative) MRI FS: 1.5T S:FLAIR Cross-sectional Systemic: 1. Presence of WMH a/w lower IL-7, but not IL-6 Vascular: 2. Presence of WMH a/w greater CD40 Romero et al. (2010)b 583 stroke-free, dementia-free subjects Framingham Offspring Study Age: 57 ± 9 Females: 57.6% Vascular: MMP-9 WMH (Quantitative) MRI FS: 1T/1.5T S: T2 Cross-sectional Vascular: 1. Presence of large WMH a/w MMP-9 Wersching et al. (2010) 447 community-dwelling, stroke-free individuals Systematic Evaluation and Alteration of Risk Factors for Cognitive Health (SEARCH) Health Study Mean age: 63 Females: 55.5% Systemic: CRP WMH (Semi-quantitative) MRI FS: 3T S: FLAIR Cross-sectional Systemic: 1. Extent of WMH not a/w CRP levels Narayan et al. (2011)b 70 hypertensive participants without dementia Age: 79.1 ± 3.5 Females: 60.0% Vascular: Homocysteine WMH (Quantitative) MRI FS: 1.5T S: FLAIR Cohort study Vascular: 1. Cross-sectionally, WMH not a/w homocysteine 2. WMH progression not a/w homocysteine Pavlovic et al. (2011) 95 SVD patients, 41 healthy subjects Healthy controls Age: 57.7 ± 10.6 Females: 53.7% SVD subjects Age: 59.8 ± 10.9 Females: 42.1% Vascular: Homocysteine WMH (Semi-quantitative) MRI FS: 1T S: T2 Cross-sectional Vascular: 1. Greater WMH severity independently a/w increased homocysteine Wada et al. (2011)b# 667 community-dwelling older adults Without lacunar infarcts Age: 67.7 ± 4.9 Females: 57.1% With lacunar infarct Age: 70.1 ± 3.5 Females: 51.6% Systemic: Fibrinogen, CRP Vascular: vWF, TM WMH (Semi-quantitative) MRI FS: nr S: FLAIR Cross-sectional Systemic: 1. Greater WMH a/w greater vWF activity and fibrinogen, but not CRP Vascular: 2. In subjects with high fibrinogen, high vWF activity and high TM increased the risk for the presence of moderate WMH Raz et al. (2012) 144 healthy subjects Age: 58.9 ± 9.1 Females: 68.1% Systemic: CRP Vascular: Homocysteine WMH (Quantitative) MRI FS: 4T S: FLAIR Cross-sectional Systemic: 1. Larger WMH volume a/w higher CRP levels Vascular: 2. Larger WMH volume a/w higher homocysteine levels Rouhl et al. (2012)b,c,d 163 lacunar stroke patients, 183 hypertensive patients, 43 healthy controls Healthy controls Age: 62.0 ± 7.7 Females: 54.5% Hypertensive patients Age: 55.3 ± 11.9 Females: 47.5% Lacunar stoke patients Age: 63.9 ± 12.0 Females: 39.0% Systemic: CRP Vascular: Neopterin, ICAM-1, VCAM-1, E-selectin, P-selectin WMH (Semi-quantitative) MRI FS: 1.5T S: FLAIR  Cross-sectional Systemic: 1. Presence of extensive WMH not a/w CRP levels Vascular: 2. Presence of high WMH a/w higher levels of neopterin and VCAM-1, but not P-selectin, ICAM-1, and E-selectin 3. Presence of both lacunes and high WMH burden a/w higher neopterin, ICAM-1, and VCAM-1 Satizabal et al. (2012)b 1,841 healthy subjects 3C-Dijon Study Age: 72.5 ± 4.1 Females: 60.4% Systemic: IL-6, CRP WMH (Quantitative) MRI FS: 1.5T S: T2 Cohort study Systemic: 1. Cross-sectionally, higher total WMH and PVH, but not DWMH a/w higher IL-6 and CRP levels 2. Longitudinally, WMH progression not a/w baseline IL-6 or CRP levels Feng L. et al. (2013)# 228 healthy, community-based subjects Singapore-Longitudinal Aging Brain Study (SLAS) Age: 65.4 ± 6.2 Females: 53.9% Vascular: Homocysteine WMH (Quantitative) MRI FS: 3T S: FLAIR Cross-sectional Vascular: 1. WMH volume not a/w homocysteine level Feng C. et al. (2013)b# 324 healthy non-stroke subjects Age: 66.9 ± 10.7 Females: 49.1% Vascular: Homocysteine WMH (Semi-quantitative) MRI FS:1.5T/ 3T S: FLAIR Cross-sectional Vascular: 1. Greater WMH burden a/w higher homocysteine level Hooshmand et al. (2013)b 103 post-mortem brains Vaanta 85+ Study Low homocysteine Age at baseline: 88.4 ± 2.8 Age at death: 92.6 ± 3.3 Females: 84.4% High homocysteine Age at baseline: 88.9 ± 3.2 Age at death: 92.6 ± 3.9 Females: 82.0% Vascular: Homocysteine WMH (Semi-quantitative) Ex vivo MRI FS: 1.5T S: nr Post-mortem study Vascular: 1. Higher post-mortem PVH, but not DWMH, a/w higher homocysteine levels at baseline Pikula et al. (2013) 1,863 stroke-free, dementia-free subjects Framingham Offspring Study Age: 61 ± 9 Females: 55% Vascular: VEGF WMH (Quantitative) MRI FS: 1T / 1.5T S: nr Cohort study Vascular: 1. WMH volume not a/w VEGF cross-sectionally Aribisala et al. (2014)c 634 participants Lothian Birth Cohort 1936 Mean age: 73 Females: nr Systemic: CRP, IL-6, fibrinogen WMH (Quantitative + Semi-quantitative) MRI FS: 1.5T S: FLAIR Cross-sectional Systemic: 1. WMH not a/w CRP, IL-6, or fibrinogen 2. Latent construct of inflammation not a/w the latent variable of WMH Rizzi et al. (2014) 135 non-stroke outpatients of cardiology clinic (34 cognitive impairment, 101 healthy controls) Age: 66.6 ± 8.7 Females: 40.0% Systemic: CRP WMH (Semi-quantitative) CT Cross-sectional Systemic: 1. Presence of WMH a/w elevated CRP levels Bridges et al. (2014) 84 post-mortem brains with minimal AD pathology (Braak 0-2) Thomas Willis Oxford Brain Collection Healthy controls post-mortem brains n = 33 Age at death: 81 ± 8 Females: 45% SVD post-mortem brains n = 51 Age at death: 83 ± 8 Female: 39% Systemic: Fibrinogen WMH (Semi-quantitative) In-life CT Post-mortem study Systemic: 1. WMH severity not a/w fibrinogen levels Kim et al. (2014)# 137 non-stroke participants Age: 64.9 ± 8.1 Females: 57.6% Systemic: IL-6, TNF-α Vascular: MMP-9, PAI-1 WMH (Semi-quantitative) MRI FS: nr S: FLAIR Cross-sectional Systemic: 1. WMH not a/w IL-6 or TNF- α Vascular: 2. Presence of high WMH a/w higher levels of MMP-9 but not PAI-1 Kloppenborg et al. (2014)b 663 patients with symptomatic atherosclerotic disease SMART-MR Study Non-Hyperhomocysteinemia n = 533 Age: 57 ± 9 Females: 20.1% Hyperhomocysteinemia n = 130 Age: 61 ± 10 Females: 13.1% Vascular: Homocysteine WMH (Quantitative) MRI FS: 1.5T S: FLAIR Cohort study Vascular: 1. Elevated homocysteine levels a/w greater WMH progression Cloonan et al. (2015) 809 acute ischaemic stroke patients Age: 65.6 ± 14.7 Females: 37% Vascular: Homocysteine WMH (Quantitative) MRI FS: 1.5T S: FLAIR Cross-sectional Vascular: 1. Higher homocysteine levels independently predicted greater WMH volume Tchalla et al. (2015) 25 community-dwelling participants (subset of 680) Maintenance Of Balance, Independent Living, Intellect, and Zest in the Elderly (MOBILIZE) Boston Study Age: 77.6 ± 6.3 Females: 68.0% Vascular: ICAM-1, VCAM-1 WMH (Quantitative) MRI FS: 3T S: PD, T2 Cross-sectional Vascular: 1. Greater WMH volume a/w higher VCAM-1 concentration, controlling for CRP, IL-6, patient characteristics and vascular risk factors Huang et al. (2015)# 110 AD and 50 healthy controls Healthy controls n = 50 Age: 73.2 ± 6.2 Females: 44.0% Mild AD n = 60 Age: 75.0 ± 6.6 Females: 53.3% Moderate to severe AD n = 50 Age: 75.4 ± 6.9 Females: 54.0% Vascular: Homocysteine, ICAM-1, VCAM-1, E-selectin WMH (Semi-quantitative) MRI FS: 1.5T S: FLAIR Cross-sectional Vascular: 1. Severity of WMH (both PVH and DWMH) a/w higherVCAM-1, but not ICAM-1 and E-selectin Gao et al. (2015)# 923 ischaemic stroke patients Age: 58.9 ± 11.9 Females: 31.6% Vascular: Homocysteine WMH (Semi-quantitative) MRI FS: nr S: FLAIR Cross-sectional Vascular: 1. Greater PVH, but not DWMH, observed in participants in the highest quartile of homocysteine 2. Greater WMH in the left and right frontal lobe observed in participants in the highest quartile of homocysteine Shoamanesh et al. (2015)b,d 1,763 stroke-free participants Framingham Offspring Study Age: 60.2 ± 9.1 Females: 53.7% Systemic: IL-6, CRP, TNF-α, TNFR2, Fibrinogen, OPG, MCP-1, MPO Vascular: ICAM-1, CD40, P-selectin, Lp-PLA2 mass and activity, homocysteine, VEGF WMH (Quantitative) MRI FS: 1T/ 1.5T S: nr Cross-sectional Systemic: 1. Presence of extensive WMH and/or silent cerebral infarcts a/w higher levels of OPG but not IL-6, CRP, TNF- α, TNFR2, fibrinogen, MCP-1, and MPO Vascular: 2. Presence of extensive WMH and/or silent cerebral infarcts a/w ICAM-1 and Lp-PLA2 mass, but not CD40, P-selectin, homocysteine, Lp-PLA2 activity, and VEGF Miwa et al. (2016)b,d# 643 subjects with more than 1 vascular risk factor Osaka Follow-up Study for Carotid Atherosclerosis (OSACA) Age: 67.2 ± 8.4 Female: 41% Vascular: Homocysteine WMH (Semi-quantitative) MRI FS: 1.5T S: FLAIR Cohort study Vascular: 1. Cross-sectionally, WMH not a/w homocysteine Mitaki et al. (2016)b,d# 519 neurologically normal subjects Age: 63.5 ± 10.3 Females: 45.3% Systemic: CRP WMH (Semi-quantitative) MRI FS: 1.5T S: FLAIR Cross-sectional Systemic: 1. WMH severity not a/w CRP levels Hainsworth et al. (2017) 126 post-mortem brains MRC-Cognitive Function and Ageing Study (CFAS) Neuropathology Study Age: 86.4 ± 7.7 Females: 54.0% Systemic: Fibrinogen WMH (Semi-quantitative) Post-morterm MRI FS: 1T S: T2 Post-mortem study Systemic: 1. Extent of fibrinogen labelling not a/w WMH, both MRI-defined and histologically defined. Wei et al. (2017)# 186 patients with ischaemic stroke and atrial fibrillation Age: 68.8 ± 12.8 Females: 65.6% Systemic: Fibrinogen WMH (Semi-quantitative) MRI FS: 3T S: FLAIR, T2 Cross-sectional Systemic: 1. Fibrinogen independently a/w greater overall WMH, PVH but not DWMH. Chung et al. (2017)b# 337 patients with intracranial atherosclerotic stroke (ICAS) and non-stroke controls Non-stroke controls n = 75 Age: 61.7 ± 10.3 Females: 58.7% ICAS n = 262 Age: 67.0 ± 12.1 Females: 42.0% Vascular: Lp-PLA2 WMH (Semi-quantitative) MRI FS: 3T S: FLAIR Cross-sectional Vascular: 1. Severity of WMH not a/w Lp-PLA2 Nam et al. (2017)b,d# 2,875 healthy subjects Age: 56 ± 9 Females: 45.6% Systemic: NLR WMH (Quantitative + Semi-quantitative) MRI FS: 1.5T S: FLAIR Cross-sectional Systemic: 1. Greater WMH grade and volume a/w higher NLR Walker et al. (2017)b,d 1,485 community participants Atherosclerosis Risk in Communities (ARIC) Study Age at midlife: 55.5 ± 5.2 Females: 62% Systemic: CRP WMH (Quantitative) MRI FS: 3T S: FLAIR Cohort study Systemic: 1. Midlife CRP a/w greater late-life WMH volume Arba et al. (2018)b 263 acute ischaemic stroke patients Age: 69 ± 13 Females: 41% Vascular: vWF, ICAM-1, VCAM-1, VEGF WMH (Semi-quantitative) CT Cohort study Vascular: 1. Cross-sectionally, presence of WMH not a/w vWF, ICAM-1, VCAM-1, VEGF 2. Longitudinally, presence of WMH a/w increased vWF, ICAM-1, VCAM-1 levels, but not VEGF Hilal et al. (2018a)b,c,d 2,814 population-based participants Rotterdam Study Age: 56.9 ± 6.5 Females: 44.8% Systemic: CRP WMH (Quantitative) MRI FS: 1.5T S: nr Cross-sectional Systemic: 1. Larger WMH volume a/w higher CRP levels You et al. (2018)# 200 acute stroke patients Without WMH n = 54 Age: 53.2 ± 11.7 Females: 33.3% With WMH n = 146 Age: 62.4 ± 12.3 Females: 41.8% Systemic: CRP, homocysteine WMH (Semi-quantitative) MRI FS: nr S: nr Cohort study Systemic: 1. Cross-sectionally, presence of WMH a/w higher levels of homocysteine and CRP Walker et al. (2018) 1,532 community-based participants ARIC Study Age: 76.5 ± 5.4 Females: 61% Systemic: CRP WMH (Quantitative) MRI FS: 3T S: FLAIR Cohort study Systemic: 1. Higher WMH a/w higher 21-year average CRP 2. Higher WMH a/w early ascending CRP levels spanning middle- to late-life Staszewski et al. (2018)b 123 SVD participants (49 with lacunar stroke, 48 with vascular dementia, 26 with vascular parkinsonism) Significance of HEmodynamic and hemostatic Factors in the course of different manifestations of Cerebral Small Vessel Disease (SHEF-SVD) Study Age: 72.2 ± 8.0 Females: 49% Systemic: CRP, IL-1α, IL-6, TNF-α Vascular: ICAM-1, P-selectin, CD40, PF-4, homocysteine WMH (Semi-quantitative) SVD progression defined as increase in WMH or development of new lacunes MRI FS: 1.5T S: FLAIR Cohort study Systemic: 1. SVD progression a/w IL-6 and domain z-score for systemic inflammation, but not CRP, TNF-α, IL-1α 2. WMH progression not a/w domain z-score for systemic inflammation, CRP, TNF-α, IL-6, IL-1α Vascular: 3. SVD progression a/w CD40, PF-4, homocysteine, but not P-selectin, ICAM-1 4. WMH progression a/w domain z-score for vascular inflammation, PF-4, but not CD40, P-selectin, ICAM-1, homocysteine Lacunes Kario et al. (1996)# 178 high-risk healthy older adults, 32 lacunar stroke patients Healthy subjects (Non-Lacunar) Age: 70 ± 7 Females: 71% Healthy subjects (Lacunar) Age: 73 ± 7 Females: 57% Lacunar stroke patients Age: 73 ± 8 Females: 44% Systemic: Fibrinogen Vascular: vWF, TM Silent lacunar infarcts MRI FS: 1.5T S: T1, T2 Cross-sectional Systemic 1. Presence of lacunes a/w higher fibrinogen levels Vascular: 2. Number of lacunes a/w higher vWF, but not TM Matsui et al. (2001)# 153 community-dwelling older adults Age: 76.7 ± 5.3 Females: 80.4% Vascular: Homocysteine Silent brain infarctions MRI FS: 1T S: T1, T2, FLAIR Cross-sectional Vascular: 1. Presence of silent brain infarctions a/w higher homocysteine Vermeer et al. (2002)a 1,077 population-based subjects Rotterdam Scan Study Age: 72.2 ± 7.4 Females: 51.5% Vascular: Homocysteine Silent brain infarcts MRI FS: 1.5T S: T1, T2 Cross-sectional Vascular: 1. Presence of silent brain infarcts a/w higher homocysteine Hassan et al. (2003)a 110 lacunar stroke patients, 50 community controls Healthy controls Age: 66.5 ± 9.7 Females: 46.0% Lacunar stroke patients Age: 67.2 ± 10.2 Females: 35.5% Vascular: ICAM-1, TM Isolated lacunar infarcts CT/MRI FS: nr S: T2/ CT Cross-sectional Vascular: 1. Greater extent of isolated lacunar infarcts a/w higher TM but not ICAM-1 levels Hassan et al. (2004)a 172 SVD patients and 172 community controls Healthy controls Age: 66.3 ± 10.2 Females: 41.9% SVD patients Age: 67.1 ± 10.3 Females: 40.7% Vascular: Homocysteine Lacunar infarctions MRI FS: nr S: nr Cross-sectional Vascular: 1. Higher homocysteine observed in patients with isolated lacunar infarcts, compared to controls Longstreth et al. (2004)a 622 non-stroke, non-TIA older adults Cardiovascular Health Study Age: 65 and above (61.3% above 75) Females: 46.6% Vascular: Homocysteine Cortical infarcts MRI FS: nr S: T1, T2 Cross-sectional Vascular: 1. Cortical infarcts not a/w homocysteine Hoshi et al. (2005)# 194 healthy subjects Age: 67.3 ± 7.5 Females: 52% Systemic: CRP, IL-6 Silent brain infarctions MRI FS: 1.5T S: T1, T2, FLAIR Cross-sectional Systemic: 1. Presence of lacunes a/w higher CRP and IL-6 levels Van Dijk et al. (2005)a 1,033 dementia-free, community-based sample Rotterdam Scan Study Age: 72 ± 7 Females: 51% Systemic: CRP Lacunar infarcts MRI FS: 1.5T S: PD, T2 Cohort study Systemic: 1. Lacunar infarcts not a/w CRP levels Pavlovic et al. (2006) 201 patients with SVD markers Age: 59.8 ± 12.6 Females: 46.8% Systemic: Fibrinogen Vascular: Homocysteine Lacunar infarcts MRI FS: 1T S: T1, T2 Cross-sectional Systemic 1. Multiple lacunes not a/w higher fibrinogen levels, compared to those with a single lacune Vascular 2. Multiple lacunes a/w higher homocysteine levels, compared to those with a single lacune Wong et al. (2006)a# 57 SVD-related stroke patients Non-Hyperhomocysteinemia Age: 68.3 ± 12.2 Females: 44.2% Hyperhomocysteinemia Age: 73.0 ± 6.8 Females: 64.3% Vascular: Homocysteine Silent brain infarcts MRI FS: 1.5T S: T1, DWI Cross-sectional Vascular: 1. Presence of lacunes not a/w homocysteine levels Schmidt et al. (2006)a 505 community-based participants (subset of 700) Austrian Stroke Prevention Study Age: 69.9 ± 7.7 Females: 53.7% Systemic: CRP Lacunes MRI FS: 1.5T S: nr Cohort study Systemic: 1. Number of lacunes not a/w CRP levels, both cross-sectionally and longitudinally Aono et al. (2007)a# 958 community-based participants Age: 66.0 ± 5.7 Females: 68% Systemic: Fibrinogen Lacunar infarcts MRI FS: 0.5T S: T2 Cross-sectional Systemic: 1. Presence of lacunar infarcts a/w higher fibrinogen levels Seshadri et al. (2008)a 1,965 healthy older adults Framingham Offspring Study Baseline age: 54 ± 10 Follow-up age: 62 ± 9 Females: 53.4% Vascular: Homocysteine Silent brain infarcts MRI FS: 1T S: PD, T2 Cohort study Vascular: 1. Presence of silent brain infarcts a/w higher baseline and concurrent homocysteine levels Wada et al. (2008)a# 689 community-based older adults Without lacunar infarcts n = 493 Age: 67.7 ± 5.0 Females: 55.2% With lacunar infarcts n = 196 Age: 69.9 ± 3.6 Females: 57.1% Systemic: CRP Lacunar infarcts MRI FS: 0.3T/ 0.5T S: T1, T2 Cross-sectional Systemic: 1. Number of lacunar infarcts not a/w CRP levels in multivariate analysis Zylberstein et al. (2008)a 526 women from community sample Without lacunes Mean age: 74.3 Females: 100% With lacunes Mean age: 73.8 Females: 100% Vascular: Homocysteine Lacunes CT Cohort study Vascular: 1. Women with highest tertile of homocysteine levels were almost three times more likely to develop lacunes after 24 years Baune et al. (2009)a 268 older community participants MEMO Study Mean age: 72.3 Females: 47.8% Systemic: IL-1β, IL-6, IL-8, IL-10, IL-12, TNF-α Lacunar infarctions MRI FS: 1.5T S: PD, T1, T2 Cross-sectional Systemic: 1. None of the inflammatory markers a/w presence of lacunar infarctions Gottesman et al. (2009) 196 with silent lacunar infarcts, 214 healthy controls ARIC Study Without infarcts Mean age: 62.5 Females: 59.8% With infarcts Mean age: 64.4 Females: 61.2% Systemic: CRP, Fibrinogen Vascular: vWF, D-Dimer, plasminogen, PAI-1, β-TG, TM, TPA antigen Lacunar infarcts MRI FS: 1.5T S: T1, T2 Cross-sectional Systemic: 1. Presence of lacunar infarcts not a/w fibrinogen or CRP Vascular: 2. Presence of lacunar infarct a/w higher vWF, higher D-Dimer, but not plasminogen, β-TG, PAI-1, TPA antigen Yoshida et al. (2009)# 97 healthy older adult volunteers Age: 65.3 ± 8.6 Females: 52.6% Systemic: CRP, IL-6, S100B Vascular: MMP-9 Silent brain infarcts MRI FS: 1.5T S: T1, T2, FLAIR Cross-sectional Systemic: 1. Presence of silent brain infarcts a/w higher CRP and IL-6 levels, but not a/w S100B Vascular: 2. Silent brain infarcts not a/w MMP-9 Romero et al. (2010)a 583 stroke-free, dementia-free subjects Framingham Offspring Study Mean age: 57 ± 9 Females: 57.6% Vascular: MMP-9 Silent cerebral infarcts MRI FS: 1T or 1.5T S: PD, T1, T2 Cross-sectional Vascular: 1. Silent cerebral infarcts not a/w MMP-9 Narayan et al. (2011)a 70 hypertensive participants without dementia Age: 791. ± 3.5 Females: 60.0% Vascular: Homocysteine Lacunes MRI FS: 1.5T S: nr Cohort study Vascular: 1. Presence and development of new lacunes not a/w homocysteine (small incidence of lacunes) Wada et al. (2011)a# 667 community-dwelling older adults Without lacunar infarcts Age: 67.7 ± 4.9 Females: 57.1% With lacunar infarct Age: 70.1 ± 3.5 Females: 51.6% Systemic: Fibrinogen, CRP Vascular: vWF, TM Lacunar infarcts MRI FS: nr S: T1, T2 Cross-sectional Systemic: 1. Presence of lacunar infarcts a/w fibrinogen but not CRP levels 2. Multiple lacunar infarcts (>2) a/w higher fibrinogen compared to single/no infarcts Vascular: 3. Presence of lacunar infarcts a/w TM but not vWF activity Rouhl et al. (2012)a,c,d 163 lacunar stroke patients, 183 hypertensive patients, 43 healthy controls Lacunar stoke patients Age: 63.9 ± 12.0 Females: 39.0% Hypertensive patients Age: 55.3 ± 11.9 Females: 47.5% Healthy controls Age: 62.0 ± 7.7 Females: 54.5% Systemic: CRP Vascular: Neopterin, ICAM-1, VCAM-1, E-selectin, P-selectin Silent lacunar infarcts MRI FS: 1.5T S: T2, FLAIR  Cross-sectional Systemic: 1. Presence of lacunes not a/w CRP levels Vascular: 2. Presence of lacunes a/w higher levels of neopterin, P-selectin, ICAM-1, and VCAM-1, but not E-selectin 3. Presence of both lacunes and high WMH burden a/w higher neopterin, ICAM-1, and VCAM-1 Satizabal et al. (2012)a 1,841 healthy subjects 3C-Dijon Study Age: 72.5 ± 4.1 Females: 60.4% Systemic: IL-6, CRP Silent brain infarctions MRI FS: 1.5T S: T1, T2, PD Cohort study Systemic: 1. Silent brain infarctions not a/w IL-6 and CRP levels, both cross-sectionally and longitudinally (low occurrence of infarcts) Feng C. et al. (2013)a# 324 healthy non-stroke subjects Age: 66.9 ± 10.7 Females: 49.1% Vascular: Homocysteine Silent brain infarctions MRI FS: 1.5T/ 3T S: T1, T2, FLAIR Cross-sectional Vascular: 1. Greater number of lacunes a/w higher homocysteine level Hooshmand et al. (2013)a 103 post-mortem brains Vantaa 85+ Study Low homocysteine Age at baseline: 88.4 ± 2.8 Age at death: 92.6 ± 3.3 Females: 84.4% High homocysteine Age at baseline: 88.9 ± 3.2 Age at death: 92.6 ± 3.9 Females: 82.0% Vascular: Homocysteine (blood sample collected at baseline) Macroinfarcts (Cavitary lesions or solid cerebral infarcts) Visual examination of post-mortem brains Post-mortem study Vascular: 1. Cerebral infarcts not a/w homocysteine Kloppenborg et al. (2014)a 663 patients with symptomatic atherosclerotic disease SMART-MR Study Non-Hyperhomocysteinemia n = 533 Age: 57 ± 9 Females: 20.1% Hyperhomocysteinemia n = 130 Age: 61 ± 10 Females: 13.1% Vascular: Homocysteine Lacunes MRI FS: 1.5T S: T1, FLAIR Cohort study Vascular: 1. Elevated homocysteine levels a/w risk of new lacunes Nylander et al. (2015) 406 community-dwelling older adults Vasculature in Uppsala Seniors (PIVUS) Study Baseline age: 70 ± 0 Females: 48.3% Systemic: CRP Lacunar infarcts MRI FS: 1.5T S: PD, T1, T2 Cohort study Systemic: 1. Lacunar infarcts not a/w CRP Riba-Llena et al. (2014) 921 hypertensive participants, without stroke or dementia Investigating Silent Strokes in hYpertensives: a magnetic resonance imaging Study (ISSYS) Mean age: 64 Females: 51.7% Vascular: Lp-PLA2 Silent brain infarcts MRI FS: 1.5T S: T1, T2, FLAIR Cross-sectional Vascular: 1. Presence and number of silent brain infarcts a/w higher Lp-PLA2 activity 2. Lp-PLA2 independently predicted silent brain infarcts in women but not men Shoamanesh et al. (2015)a,d 1,763 stroke-free participants Framingham Offspring Study Age: 60.2 ± 9.1 Females: 53.7% Systemic: IL-6, CRP, TNF-α, TNFR2, Fibrinogen, OPG, MCP-1, MPO Vascular: ICAM-1, CD40, P-selectin, Lp-PLA2 mass and activity, homocysteine, VEGF Silent cerebral infarcts MRI FS: 1T/ 1.5T S: nr Cross-sectional Systemic: 1. Presence of extensive WMH and/or silent cerebral infarcts a/w higher levels of OPG but not IL-6, CRP, TNF- α, TNFR2, fibrinogen, MCP-1, and MPO Vascular: 2. Presence of extensive WMH and/or silent cerebral infarcts a/w ICAM-1 and Lp-PLA2 mass, but not CD40, P-selectin, homocysteine, Lp-PLA2 activity, and VEGF Mitaki et al. (2016)a,d# 519 neurologically normal subjects Age: 63.5 ± 10.3 Females: 45.3% Systemic: CRP Lacunar infarcts MRI FS: 1.5T S: T1, T2 Cross-sectional Systemic: 1. Higher number of lacunar infarcts a/w higher CRP levels Miwa et al. (2016)a,d# 643 subjects with vascular risk factor OSACA Study Age: 67.2 ± 8.4 Female: 41% Vascular: Homocysteine Lacunar infarction MRI FS: 1.5T S: T1, T2, FLAIR Cohort study Vascular: 1. Cross-sectionally, presence of lacunar infarctions higher in highest tertile of homocysteine, compared to lowest tertile Chung et al. (2017)a# 337 patients with intracranial atherosclerotic stroke (ICAS) and non-stroke controls ICAS n = 262 Age: 67.0 ± 12.1 Females: 42.0% Non-stroke controls n = 75 Age: 61.7 ± 10.3 Females: 58.7% Vascular: Lp-PLA2 Ischaemic brain lesions MRI FS: 3T S: DWI Cross-sectional Vascular: 1. Higher Lp-PLA2 a/w larger lesions, greater number of lesions, and larger cortical pattern Nam et al. (2017)a,d# 2,875 healthy subjects Age: 56 ± 9 Females: 45.6% Systemic: NLR Silent brain infarcts MRI FS: 1.5T S: T1, T2 Cross-sectional Systemic: 1. Presence of silent brain infarcts not a/w NLR Walker et al. (2017)a,d 1,485 community participants ARIC Study Age at midlife: 55.5 ± 5.2 Females: 62% Systemic: CRP Lacunar infarcts MRI FS: 3T S: FLAIR Cohort study Systemic: 1. Late-life lacunar infarcts not a/w midlife CRP Arba et al. (2018)a 263 acute ischaemic stroke patients Age: 69 ± 13 Females: 41% Vascular: vWF, ICAM-1, VCAM-1, VEGF Lacunes CT Cohort study Vascular: 1. Cross-sectionally, presence of lacunes not a/w vWF, ICAM-1, VCAM-1, VEGF 2. Longitudinally, pre-existing lacunes a/w increased VEGF levels, but not ICAM-1 or VCAM-1 Hilal et al. (2018a)a,c,d 2,814 community participants Rotterdam Study Age: 56.9 ± 6.5 Females: 44.8% Systemic: CRP Lacunes MRI FS: 1.5T S: T1, T2, FLAIR Cross-sectional Systemic: 1. Higher lacune count a/w higher CRP levels Staszewski et al. (2018)a 123 SVD participants (49 with lacunar stroke, 48 with vascular dementia, 26 with vascular parkinsonism) Significance of HEmodynamic and hemostatic Factors in the course of different manifestations of Cerebral Small Vessel Disease (SHEF-SVD) Study Age: 72.2 ± 8.0 Females: 49% Systemic: CRP, IL-1α, IL-6, TNF-α Vascular: ICAM-1, P-selectin, CD40, PF-4, homocysteine Lacunes SVD progression defined as increase in WMH or development of new lacunes MRI FS: 1.5T S: nr Cohort study Systemic: 1. SVD progression related to IL-6 and domain z-score for systemic inflammation, but not CRP, TNF-α, IL-1α 2. Development of new lacunes a/w IL-6 and domain z-score for systemic inflammation, but not CRP, TNF-α, IL-1α Vascular: 3. SVD progression a/w CD40, PF-4, homocysteine, but not P-selectin, ICAM-1 4. Development of new lacunes a/w homocysteine but not domain z-score for vascular inflammation, CD-40, PF-4, P-selectin, ICAM-1 Enlarged Perivascular Spaces Rouhl et al. (2012)a,b,d 163 lacunar stroke patients, 183 hypertensive patients, 43 healthy controls Healthy controls Age: 62.0 ± 7.7 Females: 54.5% Hypertensive patients Age: 55.3 ± 11.9 Females: 47.5% Lacunar stoke patients Age: 63.9 ± 12.0 Females: 39.0% Systemic: CRP Vascular: Neopterin, ICAM-1, VCAM-1, E-selectin, P-selectin EPVS (BG, CSO, sella media level) MRI FS: 1.5T S: nr Cross-sectional Systemic: 1. Higher number of BG-EPVS, but not sella media or CSO-EPVS a/w higher neopterin levels 2. EPVS count not a/w CRP levels Vascular: 3. Number of EPVS not a/w ICAM-1, VCAM-1, E-selectin, P-selectin Aribisala et al. (2014)a 634 participants Lothian Birth Cohort 1936 Mean age: 73 Females: nr Systemic: CRP, IL-6, fibrinogen EPVS (BG, hippocampus, CSO) MRI FS: 1.5T S: nr Cross-sectional Systemic: 1. CSO-EPVS a/w CRP, but not fibrinogen or IL-6 2. Latent construct of inflammation a/w the latent variable of EPVS Wang X. et al. (2016) 100 patients with recent lacunar or ischaemic stroke Mean age: 69 Females: nr Systemic: IL-6, TNF-α, CRP, fibrinogen Vascular: vWF, ICAM-1, D-dimer EPVS (BG only) MRI FS: 1.5T S: T2 Cross-sectional Systemic: 1. IL-6, TNF-α, CRP, and fibrinogen not a/w BG-EPVS count or volume Vascular: 2. Greater number of BG-EPVS a/w lower vWF, but not ICAM-1 and D-dimer. Hilal et al. (2018a)a,b,d 2,814 community participants Rotterdam Study Age: 56.9 ± 6.5 Females: 44.8% Systemic: CRP EPVS (whole brain) MRI FS: 1.5T S: T1 and T2 Cross-sectional Systemic: 1. Higher EPVS count a/w higher tertiles of CRP levels Cerebral Microbleeds Naka et al. (2006)a# 102 stroke patients Age: 69.5 ± 10.3 Females: 47.1% Vascular: Homocysteine CMB (whole brain) MRI FS: 1T S: T2* Cross-sectional Vascular: 1. Presence of CMB not a/w homocysteine level Koh et al. (2011)# 206 patients with first acute lacunar stroke Without CMB Age: 64.6 ± 8.7 Females: 43.3% With CMB Age: 66.7 ± 11.3 Females: 45.6% Systemic: CRP, fibrinogen Vascular: MMP-9, D-dimer CMB (whole brain) MRI FS: 1.5T S: GRE Cross-sectional Systemic: 1. Presence of CMB a/w higher MMP-9 and CRP levels, but not fibrinogen or D-dimer levels Vascular: 2. Presence of CMB a/w higher MMP-9, but D-dimer levels Dassan et al. (2012) 20 acute ischaemic stroke patients Without CMB n = 15 Age: 65.3 ± 17.2 Females: 46.7% With CMB n = 5 Age: 69.4 ± 11.2 Females: 20.0% Vascular: VEGF CMB (whole brain) MRI FS: 1.5T S: GRE Cross-sectional Vascular: 1. Presence of CMB a/w higher VEGF levels Rouhl et al. (2012)a,b,c 163 lacunar stroke patients, 183 hypertensive patients, 43 healthy controls Healthy controls Age: 62.0 ± 7.7 Females: 54.5% Hypertensive patients Age: 55.3 ± 11.9 Females: 47.5% Lacunar stoke patients Age: 63.9 ± 12.0 Females: 39.0% Systemic: CRP Vascular: Neopterin, ICAM-1, VCAM-1, E-selectin, P-selectin CMB (whole brain) MRI FS: 1.5T S: GRE Cross-sectional Vascular: 1. Higher number of CMB a/w higher E-selectin levels, irrespective of location (deep or lobar) Huang et al. (2013)# 126 patients with first ever ischaemic stroke Without CMB Age: 63.2 ± 13.3 Females: 41.2% With CMB Age: 64.6 ± 12.7 Females: 27.0% Vascular: E-selectin CMB (lobar, deep, infratentorial) MRI FS: 3T S: SWI Cross-sectional Vascular: 1. Presence and number of CMB a/w higher E-selectin levels 2. Higher E-selectin levels in patients with mixed CMB, and severe CMB, compared to those without CMB Shoamanesh et al. (2015)a,b 1,763 stroke-free participants Framingham Offspring Study Age: 60.2 ± 9.1 Females: 53.7% Systemic: IL-6, CRP, TNF-α, TNFR2, fibrinogen, OPG, MCP-1, MPO Vascular: ICAM-1, CD40, P-selectin, Lp-PLA2 mass and activity, homocysteine, VEGF CMB (lobar-only, deep-only, any-deep) MRI FS: 1T/ 1.5T S: GRE Cross-sectional Systemic: 1. Presence of CMB a/w higher TNFR2 and MPO – this was most prominent in persons with only deep CMB 2. Presence of CMB not a/w levels of IL-6, CRP, TNF-α, fibrinogen, OPG, and MCP-1 3. Higher number of CMB a/w higher TNFR2 and MPO, but not IL-6, CRP, TNF-α, fibrinogen, OPG, and MCP-1 Vascular: 4. Presence of CMB not a/w CD40, ICAM-1, P-selectin, homocysteine, LP-PLA2 activity and mass, and VEGF Mitaki et al. (2016)a,b# 519 neurologically normal subjects Age: 63.5 ± 10.3 Females: 45.3% Systemic: CRP CMB (subcortical white matter, BG, thalamus) MRI FS: 1.5T S: GRE Cross-sectional Systemic: 1. Number of CMB not a/w CRP levels Miwa et al. (2016)a,b# 643 subjects with vascular risk factor OSACA Study Age: 67.2 ± 8.4 Female: 41% Vascular: Homocysteine CMB (strictly lobar, strictly deep, mixed) MRI FS: 1.5T S: GRE Cohort study Vascular: 1. Cross-sectionally, presence of total CMB and deep CMB, but not strictly lobar CMB, were more prevalent in the highest tertile of homocysteine, compared to the lowest tertile. Wang B.R. et al. (2016) 112 ischaemic stroke patients Mean age: 67.1 Females: 28.6% Vascular: Homocysteine CMB (lobar, deep, mixed) MRI FS: 3T S: SWI Cross-sectional Vascular: 1. Presence and number of CMB a/w higher homocysteine levels Zhang J.B. et al. (2016)# 146 AD patients Age: 72.1 ± 7.4 Females: 56.8% Vascular: VEGF CMB (strictly lobar, strictly non-lobar, mixed) MRI FS: 3T S: SWI Cross-sectional Vascular: 1. Presence and number of CMB a/w higher VEGF Lu et al. (2017)# 201 stroke-free participants Non-CMB group Age: 61.76 ± 11.06 Females: 48.0% CMB group Age: 68.61 ± 7.76 Females: 40.8% Systemic: CRP, IL-6 Vascular: MMP-9 CMB (deep/infratentorial, lobar) MRI FS: 3T S: SWI Cross-sectional Systemic: 1. Presence of deep/infratentorial and deep CMB a/w CRP, IL-6 Vascular: 2. Presence of deep/infratentorial and deep CMB a/w MMP-9 Nam et al. (2017)a,b# 2,875 healthy subjects Age: 56 ± 9 Females: 45.6% Systemic: NLR CMB (whole brain) MRI FS: 1.5T S: GRE Cross-sectional Systemic: 1. Presence of CMB not a/w NLR levels Walker et al. (2017)a,b 1,485 community participants ARIC Study Age at midlife: 55.5 ± 5.2 Females: 62% Systemic: CRP CMB (whole brain) MRI FS: 3T S: GRE Cohort study Systemic: 1. Presence of late-life CMB not a/w midlife CRP levels Hilal et al. (2018a)a,b,c 2,814 community participants Rotterdam Study Age: 56.9 ± 6.5 Females: 44.8% Systemic: CRP CMB (lobar, deep, infratentorial) MRI FS: 1.5T S: GRE Cross-sectional Systemic: 1. Elevated CRP a/w greater number of deep/infratentorial CMB, but fewer lobar CMB Wu et al. (2018)# 148 acute ischaemic stroke patients Age: 69.6 ± 8.2 Females: 47.3% Vascular: ICAM-1 CMB (whole brain) MRI FS: 1.5T S: SWI Cross-sectional Vascular: 1. Presence of CMB a/w higher ICAM-1 a Study also covered under WMH b Study also covered under Lacunes c Study also covered under EPVS d Study also covered under CMB # Studies conducted in Asian cohorts Abbreviations: AD, Alzheimer’s disease; a/w, associated with; BG, basal ganglia; β-TG, β-thromboglobulin; CD40, CD40 ligand; CRP, C-reactive protein; CSO, centrum semiovale; CT, computed tomography; CVD, cerebrovascular disease; DWI, diffusion weighted imaging; DWMH, deep white matter hyperintensities; EPVS, enlarged perivascular spaces; FLAIR, fluid-attenuated inversion recovery; GFAP, glial fibrillary acidic protein; GRE, T2* gradient-recalled echo; ICAM-1, intercellular adhesion molecule-1; IL-6, interleukin-6; LA-DR, human Leukocyte Antigen – DR isotype; Lp-PLA2, lipoprotein-associated phospholipase A2; MCP-1, monocyte chemotactic protein 1; MPO, myeloperoxidase; MRI, magnetic resonance imaging; NLR, neutrophil to lymphocyte ratio; nr, not reported; OPG, osteoprotegerin; PAI-1, plasminogen activator inhibitor-1; PD, proton density; PVH, periventricular white matter hyperintensities; SVD, small vessel disease; SWI, susceptibility weighted imaging; TM, thrombomodulin; TNF-α, tumour necrosis factor-α; TNFR2, tumour necrosis factor receptor-2; TPA, tissue plasminogen activator; VEGF, vascular endothelial growth factor; vWF, von Willebrand factor; WMH, white matter hyperintensities. image3.tiff image1.tiff image2.tiff