ABSTRACT We aimed to investigate the association between nonalcoholic fatty liver disease (NAFLD) and cerebral small vessel disease (CSVD) burden, especially according to the NAFLD severity.

A total of 1,260 participants were included. The CSVD burden was assessed with white matter hyperintensities (WMH), lacunes, and microbleeds (MBs) on brain MRI. An ultrasound diagnosis of

fatty liver was made based on standard criteria, and the Fibrosis-4 (FIB-4) index was used to classify participants with NAFLD with having a high-intermediate (FIB-4 ≥1.45) or low (FIB-4 

< 1.45) probability of advanced fibrosis. A multivariable logistic regression analysis was used to assess the association between NAFLD and the presence of moderate to severe WMH,

NAFLD with fibrosis, has a significant association with the presence of moderate to severe WMH in cognitively normal individuals, and NAFLD severity predicted more frequent moderate to

severe WMH. SIMILAR CONTENT BEING VIEWED BY OTHERS AMYLOID DEPOSITION AND SMALL VESSEL DISEASE ARE ASSOCIATED WITH COGNITIVE FUNCTION IN OLDER ADULTS WITH TYPE 2 DIABETES Article Open access

01 February 2024 LOBAR MICROBLEEDS ARE ASSOCIATED WITH COGNITIVE IMPAIRMENT IN PATIENTS WITH LACUNAR INFARCTION Article Open access 02 October 2020 ASSOCIATION BETWEEN TOTAL CEREBRAL SMALL

VESSEL DISEASE SCORE AND COGNITIVE FUNCTION IN PATIENTS WITH VASCULAR RISK FACTORS Article 09 March 2023 INTRODUCTION Nonalcoholic fatty liver disease (NAFLD) is the most common cause of

chronic liver disease, and the prevalence is rapidly increasing worldwide1,2. NAFLD has attracted growing attention in terms of its relation with not only hepatic complications, but also

with cardiometabolic risk factors, such as hypertension, insulin resistance, and obesity3. There is increasing evidence that NAFLD may affect brain health. A recent study showed that NAFLD

is associated with learning and memory, as measured by symbol digit learning tests4. However, the pathobiology of this association remains unclear. Cerebral small vessel diseases (CSVD),

such as white matter hyperintensities (WMH), lacunes, and microbleeds (MBs), are major causes of cognitive impairment. Studies have demonstrated an association between certain

cardiometabolic risk factors, such as hypertension and diabetes, with CSVD development. Considering the role of NAFLD as an independent cardiometabolic risk factor5,6,7, it is reasonable to

expect NAFLD to be associated with the development of CSVD. In fact, a recent study revealed that the fibrosis severity of NAFLD predicted the presence of WMH8. However, this study

investigated a small number of study subjects, and the relationships between NAFLD and other CSVD markers, such as lacunes and MBs, which have different pathobiologies from WMH, have not

been thoroughly studied. Therefore, in this study, we aimed to investigate the association between NALFD, as assessed by ultrasonography (US), and CSVD burden, measured by magnetic resonance

imaging (MRI), in a large sample of cognitively normal individuals. We hypothesized that: (1) NAFLD is associated with the presence of lacunes and MBs, as well as WMH, independent of other

cardiometabolic risk factors; and (2) their significant association might depend on NAFLD severity. RESULTS SUBJECTS CHARACTERISTICS Among 1,260 patients, 498 patients (39.6%) had NAFLD at

baseline and 239 patients (19.0%) were categorized as having NAFLD with an intermediate to high FIB-4 (≥1.45) (Table 1). The demographic data of categorized patients according to NFS is

shown in Table 2. Participants with NAFLD were more likely to be older, male, smokers, moderate alcohol drinkers, and to have a higher BMI and a higher frequency of hypertension and diabetes

compared to those without NAFLD. In terms of CSVD markers, while only 9.6% of patients without NAFLD had moderate to severe WMH, 13.1% of patients with NAFLD with a low FIB-4 (<1.45) and

18.8% of NAFLD patients with an intermediate to high FIB-4 (≥1.45) had moderate to severeWMH. These results were statistically different. However, the prevalence of MBs and lacunes did not

vary among the three groups. ASSOCIATION BETWEEN NAFLD AND CSVD MARKERS First, the crude odds ratio (OR) for moderate to severe WMH comparing participants with NAFLD to those without it was

1.78 (95% confidence interval (CI): 1.27–2.50). (Model 1). This association remained significant after adjusting for age, sex, smoking, alcohol, obesity, hypertension, diabetes, and

hyperlipidemia (OR: 1.64; 95% CI: 1.10–2.42). However, the associations between NAFLD and the presence of lacunes and MBs were not significant, as crude ORs for lacunes and MB were 1.19 (95%

CI: 0.81–1.76) and 1.15 (95% CI: 0.66–1.55), respectively. When we assessed these associations according to the severity of NAFLD, the OR (95% CI) for moderate to severe WMH in participants

with a low FIB-4 (<1.45) and with intermediate to high FIB-4 (≥1.45) were 1.14 (0.72–1.82) and 1.77 (1.13–2.78) compared to participants without NAFLD, respectively. Especially, the

linear trend test showed a significant association between the severity of NAFLD fibrosis (non-NAFLD, NAFLD with FIB-4 < 1.45 or NAFLD with FIB-4 ≥1.45) and the presence of moderate to

severe WMH (_p_ for trend = 0.016) (Table 3). When the analyses were conducted with NAFLD categories using NFS, the results were same as those seen with FIB-4 index, as the association

between the presence of moderate to severe WMH and NAFLD with a low NFS (<−1.455) was not significant (OR: 0.92; 95% CI: 0.53–1.59), while its association with an intermediate to high NFS

(≥−1.455) was significant (OR: 2.05; 95% CI: 1.34–3.14). A linear trend test showed a significant association between the severity of NAFLD fibrosis and the presence of moderate to severe

WMH as well (_p_ for trend = 0.002) (Table 4). Additionally, we evaluated the association between NAFLD with FIB-4 ≥1.45 with the prevalence of moderate or severe WMH in different clinical

subgroups (Fig. 1). The level of association between NAFLD with FIB-4 ≥1.45 and the presence of moderate to severe WMH was significantly different according to the educational level (_p_ for

interaction = 0.03). DISCUSSION This study determined that NAFLD has a significant association with the presence of WMHs, even after controlling for cardiometabolic risk factors. This study

included a large number of participants (n = 1,260) and extensive data about comorbid cardiometabolic risk factors and various CSVD MRI markers, key issues in older patients. These

strengths enabled us to examine the independent relationship between NAFLD and CSVD, regardless of various cardiometabolic risk factors. Therefore, our findings provide insight into the

contribution of NAFLD to development of CSVD. Our major finding was that NAFLD has a significant association only with WMH among different CSVD markers, even after controlling for

cardiometabolic risk factors. Especially, when we divided patients into two groups according to fibrosis score, association with WMH was maintained only in NAFLD with an intermediate to

severe fibrosis score, suggesting that the severity of NAFLD, rather than the presence of NAFLD itself, might be more importantly related to the development of CSVD. This finding is

consistent with a recent study which reported that patients with NAFLD with fibrosis have a higher risk of having WMH than patients without NALFD or patients with NALFD without fibrosis8.

However, our findings showed that there was an increase in the frequency of moderate to severe WMH with increasing fibrosis severity. It is difficult to clarify the exact pathomechanism of

the association between NAFLD and WMH. However, there are several explanations for this finding. First, NAFLD contributes to decreased cerebrovascular reactivity, causing chronic

hypoperfusion and subsequent WMH. The pathogenesis of WMH has not been identified, but endothelial dysfunction could decrease cerebrovascular reactivity9 and chronic hypoperfusion. NAFLD is

closely related to insulin resistance10 and metabolic risk factors, which is significantly associated with endothelial dysfunction11. Second, NAFLD could be related to subclinical

inflammation, similar to other metabolic syndromes which contribute to blood brain barrier (BBB) disruption12, and progress to WMH13, despite the unknown relationship between systemic

inflammation and CSVD. NAFLD with advanced fibrosis might contribute to systemic inflammation more than NAFLD without fibrosis. Finally, NAFLD is related to carotid atherosclerosis14, which

might cause microembolic events, which could in turn lead to increased WMH. The aforementioned processes could also be interrelated, rather than occurring independently. The level of

association between NAFLD and the presence of moderate to severe WMH was significantly different according to the educational level of the patients (_p_ for interaction = 0.03). This

observation has led us to consider the reason for the effect of different educational levels on the NAFLD-WMH association. Interestingly, the lower educational group had a stronger

association between NAFLD and WMH. One of possible explanations for this is that education is a representative measure of socioeconomic status (SES)15. Therefore, patients with a lower SES

might have more health-related risk factors, including cardiovascular risk factors16 and other unmeasured confounding variables, such as dietary and lifestyle risk factors, which make

patients in this group more vulnerable to developing WMH in response to the same metabolic stress. In this study, NAFLD was not associated with the formation of lacunes or MBs. Unlike our

findings, a recent study showed fatty liver disease is reported to be associated with lacunar infarct especially in non-obese population17. However, they included alcoholic fatty liver

disease as well as NAFLD. WMH, lacunes, and MBs are CSVD MRI markers, but they have slightly different pathogeneses. WMH are related to chronic, diffuse, and subclinical ischemia, and BBB

disruption18,19. Lacunes are attributed to acute, severe, or localized ischemia18, and MBs result from focal bleeding in small vessels that are either damaged by lipohyalinosis or by amyloid

angiopathy20. Therefore, we can assume that NAFLD may lead to increased inflammation or cerebrovascular reactivity, and cause diffuse and chronic hypoperfusion or BBB disruption, rather

than focal ischemia or microhemorrhages. In this study, we investigated the relationship between CSVD and NAFLD in a large study with cognitively normal individuals. However, there were

several limitations to this study. First, our study was cross-sectional, precluding claims of causality. The temporal relationship between NAFLD and CSVD markers remains unclear.

Longitudinal studies are needed to determine the impact of NAFLD on the incidence of CSVD. Second, we defined NAFLD by US, which cannot detect mild steatosis and is operator-dependent. We

also calculated the NAFLD FIB-4/NFS index to represent the severity of NAFLD, because simple US cannot differentiate steatohepatitis from simple steatosis21. In addition, while the accuracy

of US for establishing the presence of fatty liver is high, it is subject to measurement error22. Finally, this study was conducted in Koreans who underwent health screening exams, and thus

may not be generalizable to other settings or to other ethnicities. In conclusion, our findings suggest that NAFLD might be a potential independent risk factor for the presence of moderate

to severe WMH. Therefore, evaluation of conventional risk factors in patients with NAFLD warrants the presence of CSVD. This is of particular importance, given that there is an increasing

prevalence of NAFLD, which can be potentially controlled by lifestyle modification, such as weight reduction, regular exercise, and changes in dietary patterns. Therefore, additional efforts

to evaluate and manage NAFLD as one of several important cardiometabolic risk factors in patients with CSVD are needed. METHODS STUDY PARTICIPANTS The study population was comprised of men

and women 40 years of age or older who underwent a health screening exam at the Health Promotion Center of the Samsung Medical Center in Seoul, Korea from October 1, 2008 to December 31,

2013. We included 2,320 subjects who had undergone at least one neurological and neurocognitive screening test, including brain MRI and the Mini-Mental Status Examination (MMSE), as well as

an abdominal US. We excluded participants who had any of the following conditions: history of cancer (n = 210), history of liver cirrhosis/positive hepatitis B surface antigen/hepatitis C

virus antibodies (n = 165), alcohol intake ≥30 g/day in men or ≥20 g/day in women (n = 489), or that scored below the 16th percentile in age-, sex-, and education-matched norms according to

the MMSE (n = 108). Patients that had structural lesions, including territorial cerebral infarctions, brain tumors, or intracranial hemorrhage on brain MRI were also excluded (n = 23). In

addition, we excluded participants who had missing data on alcohol intake (n = 117), education, or MMSE score (n = 89), resulting in a final sample size of 1,260 (640 men and 620 women).

(Fig. 2). This study was approved by the Institutional Review Board of the Samsung Medical Center. The requirement for informed consent was waived because we only used de-identified data

collected for clinical purposes during health screening exams. DATA COLLECTION To evaluate the presence of NAFLD, an abdominal US was performed using the LogiQ E9 (GE Healthcare, Milwaukee,

WI, USA), iU22 xMatrix (Philips Medical Systems, Cleveland, OH, USA) or ACUSON Sequoia 512 (Siemens, Issaquah, WA, USA) US machine, and the procedure was performed by experienced

radiologists blinded to the study aims. Images were captured in a standard fashion with the patients in the supine position with their right arm raised above their head. A US diagnosis of

fatty liver was made based on standard criteria, including parenchymal brightness, liver-to-kidney contrast, deep beam attenuation, and bright vessel walls23,24. Because we excluded

participants with excessive alcohol use (≥30 g/day for men and ≥20 g/day for women), as well as other identifiable causes of fatty liver at baseline, a US-diagnosed fatty liver was

considered NAFLD. To assess the severity of fibrosis, we calculated Fibrosis-4 (FIB-4) index as (age (years) x AST (U/L))/(platelet (109/L) x (ALT(U/L)1/2), and stratified NAFLD patients

according to their FIB-4 index as having an intermediate to high (FIB-4 ≥1.45) or low (FIB-4 < 1.45) probability of advanced fibrosis25. A low FIB-4 (<1.45) is also a strong predictor

of the absence of liver fibrosis. For sensitivity analysis, we also classified patients into two groups using NAFLD fibrosis score (NFS), which was calculated as −1.675 + 0.037 × age (years)

 + 0.094 × BMI (kg/m2) + 1.13 × impaired fasting glucose/diabetes (yes = 1, no = 0) + 0.99 × AST/ALT ratio − 0.013 × platelet count (×109/l) − 0.66 × albumin (g/dl)26, with subjects having

either a high-intermediate (NFS ≥ −1.455) or low (NFS < −1.455) probability of advanced fibrosis. BRAIN MRI All participants underwent brain MRI, including T2* gradient echo (GRE) and

three-dimensional (3D) Fluid-attenuated inversion recovery (FLAIR) imaging, using the same type of 3.0 T MRI scanner (Philips 3.0 T Achieva, Best, the Netherlands). The following parameters

were used for the T2* GRE images: axial slice thickness, 5.0 mm; inter-slice thickness, 2 mm; repetition time (TR), 669 ms; echo time (TE) 16 ms; flip angle, 18°; matrix size, 560 × 560

pixels. 3D FLAIR images were acquired with the following imaging parameters: axial slice thickness, 2 mm; no gap; TR, 11,000 ms; TE, 125 ms; flip angle, 90°; and matrix size, 512 × 512

pixels. CSVD MARKERS ON MRI We used a modified Fazekas scale to visually rate WMH27. Periventricular WMH (PWMH) were classified as P1 (cap or band <5 mm), P2 (5 mm ≤ cap or band <10 

mm), or P3 (cap or band ≥10 mm); deep WMH (DWMH) were classified into D1 (maximum diameter of deep white matter lesion <10 mm), D2 (10 mm ≤ lesion  <25 mm), or D3 (≥λ25 mm). The

intra-class correlation coefficients for the inter-rater reliability of the WMH visual rating scale ranged from 0.73 and 0.91. PWMH and DWMH ratings were combined to give a final WMH

classification of minimal (D1P1 or D1P2), moderate (D2P1, D3P1, D2P2, D3P2, D1P3, and D2P3), or severe (D3P3). In this study, the presence of WMH was determined when patients had moderate or

severe WMH. This classification discriminates the presence of vascular risk factors and the severity of cerebrovascular disease markers28. Two experienced neurologists blinded to patient

data reviewed images to determine the number and location of lacunes and MBs according to neuroimaging standards29. The kappa values for agreement between the two neurologists for the

presence of lacunes and MBs were 0.78 and 0.92, respectively. CARDIOMETABOLIC RISK FACTORS Serum total cholesterol, triglycerides, high-density lipoprotein (HDL) cholesterol, and low-density

lipoprotein (LDL) cholesterol levels were determined using an enzymatic colorimetric method. Hyperlipidemia was defined according to the Adult Treatment Panel III30 criteria as triglyceride

levels ≥150 mg/dl, HDL-cholesterol levels <40 mg/dl, or use of medication for dyslipidemia. Glucose was measured in blood samples that were collected after at least 10 hours of fasting.

Diabetes was defined as a fasting serum glucose ≥126 mg/dL or self-reported use of insulin or antidiabetic medications. The Department of Laboratory Medicine and Genetics at the Samsung

Medical Center has participated in several proficiency testing programs operated by the Korean Association of Quality Assurance for Clinical Laboratory, the Asian Network of Clinical

Laboratory Standardization and Harmonization, and the College of American Pathologists. Smoking status was categorized as never, past, or current smoker. Alcohol intake was categorized as

either none or moderate (<30 g/day in men and <20 g/day in women). Trained nurses measured the height, weight, and sitting blood pressure of participants wearing a lightweight hospital

gown and no shoes. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. BMI was classified according to Asian-specific criteria31, and obesity

was defined as a BMI ≥25 kg/m2. Hypertension was defined as a systolic blood pressure ≥140 mm Hg, a diastolic blood pressure ≥90 mmHg, or current use of antihypertensive medications.

STATISTICAL ANALYSIS An analysis of variance was used to compare the demographics and clinical characteristics of three groups: no NAFLD, NAFLD with a low FIB-4 (<1.45) and NAFLD with an

intermediate to high FIB-4 (≥1.45). After the test, we conducted post hoc test using Scheffé's method. A multivariable logistic regression analysis was performed to assess the

association between NAFLD and CSVD markers. We also conducted the same analysis for three groups categorized by NFS: no NAFLD, NAFLD with a low NFS (< −1.455) and NAFLD with an

intermediate to high NFS (≥−1.455). We used three models with increasing degrees of adjustment to account for potential confounding factors at baseline. To address missing data, we used a

missing-indicator method to create a missing value category for each incomplete, independent, categorical variable32. The crude model was not adjusted for confounders. Model 1 was adjusted

for age and sex. Model 2 was further adjusted for smoking (never vs. past or current smokers), alcohol consumption (none vs. moderate), obesity (not obese vs. obese), and metabolic factors,

including hypertension, diabetes, and hyperlipidemia. Additionally, to test for linear trends according to NAFLD severity, we included the NAFLD severity category (no NAFLD, NAFLD with a low

probability of fibrosis (FIB-4 < 1.45 or NFS < −1.455) and NAFLD with an intermediate to high probability of fibrosis (FIB-4 ≥ 1.45 or NFS ≥−1.455)) as a continuous variable in the

logistic regression models. In addition, we explored the association between NAFLD with FIB-4 ≥1.45 and the CSVD markers in pre-specified clinically relevant subgroups defined by age (<65

vs. ≥65 years), sex (women vs. men), education (<middle school vs. ≥middle school), smoking (never vs. past or current smokers), alcohol consumption (none vs. moderate), obesity (not

obese vs. obese), hypertension (yes vs. no), diabetes (yes vs. no), and hyperlipidemia (yes vs.no). We tested for the interaction of NAFLD with clinical characteristics using Wald tests for

cross-product terms in regression models. All reported p-values were two-sided, and the significance level was set at 0.05. All analyses were performed using STATA version 14 (StataCorp LP,

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ACKNOWLEDGEMENTS This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2017R1A2B2005081) and the Brain Research Program

through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2016M3C7A1913844). AUTHOR INFORMATION Author notes * Hyemin Jang, Danbee

Kang, Juhee Cho and Sang Won Seo contributed equally. AUTHORS AND AFFILIATIONS * Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351,

Korea Hyemin Jang, Yeshin Kim, Young Kyoung Jang, Hee Jin Kim, Duk L. Na & Sang Won Seo * Neuroscience Center, Samsung Medical Center, 06351, Seoul, Korea Hyemin Jang, Yeshin Kim, Young

Kyoung Jang, Hee Jin Kim, Duk L. Na & Sang Won Seo * Center for Clinical Epidemiology, Samsung Medical Center, 06351, Seoul, Korea Danbee Kang, Eliseo Guallar & Juhee Cho * Health

Promotion Center, Samsung Medical Center, 06351, Seoul, Korea Hee Young Shin & Mira Kang * Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan

University, School of Medicine, Seoul, Korea Yoosoo Chang * Department of Neurology, Kyung Hee University Hospital, Seoul, Korea Jin San Lee * Department of Neurology, Chonbuk National

University Hospital, Chonbuk National University Medical School, Jeonju, Korea Ko Woon Kim * Department of Health Sciences and Technology, Sungkyunkwan University, Seoul, 06351, Korea Juhee

Cho * Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, 06351, Korea Eliseo Guallar & Juhee Cho * Department of Epidemiology, Johns Hopkins Medical

Institutions, Baltimore, USA Eliseo Guallar * Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, USA Eliseo Guallar * Welch Center for Prevention, Epidemiology and

Clinical Research, Johns Hopkins Medical Institutions, Baltimore, USA Eliseo Guallar Authors * Hyemin Jang View author publications You can also search for this author inPubMed Google

Scholar * Danbee Kang View author publications You can also search for this author inPubMed Google Scholar * Yoosoo Chang View author publications You can also search for this author

inPubMed Google Scholar * Yeshin Kim View author publications You can also search for this author inPubMed Google Scholar * Jin San Lee View author publications You can also search for this

author inPubMed Google Scholar * Ko Woon Kim View author publications You can also search for this author inPubMed Google Scholar * Young Kyoung Jang View author publications You can also

search for this author inPubMed Google Scholar * Hee Jin Kim View author publications You can also search for this author inPubMed Google Scholar * Duk L. Na View author publications You can

also search for this author inPubMed Google Scholar * Hee Young Shin View author publications You can also search for this author inPubMed Google Scholar * Mira Kang View author

publications You can also search for this author inPubMed Google Scholar * Eliseo Guallar View author publications You can also search for this author inPubMed Google Scholar * Juhee Cho

View author publications You can also search for this author inPubMed Google Scholar * Sang Won Seo View author publications You can also search for this author inPubMed Google Scholar

CONTRIBUTIONS H.J., D.K.: design of the study, acquisition and analysis of the data, drafting the manuscript. Y.K., K.W.K., J.S.L., Y.K.J., H.J.K., H.Y.S., M.K.: acquisition of the data.

Y.C., D.L.N., E.G.: interpretation of the data, revising the manuscript. S.W.S., J.C.: design of the study, acquisition and analysis of the data, interpretation of the data, revising the

manuscript, study supervision. CORRESPONDING AUTHORS Correspondence to Juhee Cho or Sang Won Seo. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests.

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al._ Non-alcoholic fatty liver disease and cerebral small vessel disease in Korean cognitively normal individuals. _Sci Rep_ 9, 1814 (2019). https://doi.org/10.1038/s41598-018-38357-x

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