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, lacunes, and MBs. NAFLD had a significant association only with moderate to severe WMH (OR: 1.64, 95% CI: 1.10–2.42), even after controlling for cardiometabolic risk factors. A linear trend test showed a significant association between the severity of NAFLD fibrosis and the presence of moderate to severe WMH (p for trend <0.001). Our findings suggest that NAFLD, especially 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.
Subject terms: Non-alcoholic fatty liver disease, Cerebrovascular disorders
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 factor5–7, 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.
Table 1.
Study participant characteristics according to NAFLD FIB-4 score (n = 1,260).
| Characteristic | NAFLD | p value | ||
|---|---|---|---|---|
| No | Yes | |||
| (n = 762) | FIB-4 < 1.45 (n = 259) | FIB-4 ≥ 1.45 (n = 239) | ||
| Age (years) | 63.8 (7.4) | 61.1 (6.4)* | 66.5 (6.2)*† | <0.01 |
| Female sex | 410 (53.8) | 133 (51.4) | 77 (32.2) | <0.01 |
| >middle school education | 306 (40.2) | 117 (45.2) | 105 (43.9) | 0.29 |
| MMSE (%) | 27.9 (1.8) | 28.2 (1.6) | 27.6 (2.1) | <0.01 |
| Hypertension | 339 (44.5) | 146 (56.4) | 155 (64.9) | <0.01 |
| Diabetes | 95 (12.5) | 61 (23.6) | 60 (25.1) | <0.01 |
| Hyperlipidemia | 233 (30.6) | 96 (37.1) | 91 (38.1) | 0.13 |
| BMI (kg/m2) | 23.0 (2.5) | 25.3 (2.8)* | 25.6 (2.7)* | <0.01 |
| Smoking | <0.01 | |||
| Never | 453 (59.5) | 138 (53.3) | 113 (47.3) | |
| Past or current smoker | 249 (32.7) | 101 (39.0) | 110 (46.0) | |
| Unknown | 60 (7.9) | 20 (7.7) | 16 (6.7) | |
| Alcohol intake | <0.01 | |||
| None | 494 (64.8) | 163 (62.9) | 121 (50.6) | |
| Moderate | 268 (35.2) | 96 (37.1) | 118 (49.4) | |
| Systolic blood pressure (mmHg) | 123.6 (18.0) | 125.2 (16.7) | 127.3 (18.0)* | 0.02 |
| Diastolic blood pressure (mmHg) | 73.5 (10.5) | 76.0 (10.4)* | 76.6 (10.5)* | <0.01 |
| Fasting glucose (mg/dL) | 95.0 (14.7) | 106.4 (24.9)* | 104.5 (21.2)* | <0.01 |
| HDL cholesterol (mg/dL) | 58.8 (15.1) | 51.2 (13.3)* | 50.2 (11.7)* | <0.01 |
| Triglycerides (mg/dL) | 89 (67–121) | 133 (98–184)* | 129 (88–176)* | <0.01 |
| Total cholesterol (mg/dL) | 193.2 (36.3) | 194.4 (37.4) | 191.8 (35.6)† | 0.73 |
| CSVD markers (≥1) | 165 (21.6) | 61 (23.6) | 81 (33.9) | <0.01 |
| Presence of moderate to severe WMH | 73 (9.6) | 34 (13.1) | 45 (18.8) | <0.01 |
| Presence of microbleeds | 74 (9.71) | 24 (9.3) | 31 (13.0) | 0.30 |
| Presence of lacunes | 64 (8.4) | 19 (7.3) | 30 (12.7) | 0.09 |
Data are presented as means (SDs) or numbers (percentages).
NAFLD nonalcoholic fatty liver disease, FIB-4 Fibrosis-4 index, BMI body mass index, HDL high-density lipoprotein, MMSE Mini-Mental State Examination, CSVD cerebral small vessel disease, WMH white matter hyperintensities.
*P < 0.05 compared to no NAFLD; †P < 0.05 compared to FIB-4 < 1.45.
Table 2.
Study participant characteristics according to NAFLD NFS score (n = 1,260).
| Characteristic | NAFLD | p value | ||
|---|---|---|---|---|
| No | Yes | |||
| (N = 762) | NFS < −1.455 (n = 197) | NFS ≥ −1.455 (n = 301) | ||
| Age (years) | 63.8 (7.4) | 61.3 (6.4)* | 65.2 (6.7)*† | <0.01 |
| Female sex | 410 (53.8) | 106 (53.8) | 104 (34.6) | <0.01 |
| >middle school education | 306 (40.2) | 89 (45.2) | 133 (44.2) | 0.29 |
| MMSE (%) | 27.9 (1.8) | 28.2 (1.6) | 27.6 (2.1) | <0.01 |
| Hypertension | 339 (44.5) | 102 (51.8) | 199 (66.1) | <0.01 |
| Diabetes | 95 (12.5) | 19 (9.6) | 102 (33.9) | <0.01 |
| Hyperlipidemia | 233 (30.6) | 75 (38.1) | 112 (37.2) | 0.13 |
| BMI (kg/m2) | 23.0 (2.5) | 24.4 (2.5)* | 26.1 (2.7)*† | <0.01 |
| Smoking | 0.001 | |||
| Never | 453 (59.5) | 111 (56.4) | 140 (46.5) | |
| Past or current smoker | 249 (32.7) | 70 (35.5) | 141 (46.8) | |
| Unknown | 60 (7.9) | 16 (8.1) | 20 (6.6) | |
| Alcohol intake | <0.01 | |||
| None | 494 (64.8) | 134 (68.0) | 150 (49.8) | |
| Moderate | 268 (35.2) | 63 (32.0) | 151 (50.2) | |
| Systolic blood pressure (mmHg) | 123.6 (18.0) | 124.6 (16.8) | 127.2 (17.6)* | 0.01 |
| Diastolic blood pressure (mmHg) | 73.5 (10.5) | 75.4 (10.7) | 76.9 (10.2)* | <0.01 |
| Fasting glucose (mg/dL) | 95.0 (14.7) | 97.8 (20.2) | 110.5 (23.7)*† | <0.01 |
| HDL cholesterol (mg/dL) | 58.8 (15.1) | 50.6 (13.2)* | 50.8 (12.1)* | <0.01 |
| Triglycerides (mg/dL) | 89 (67–121) | 132 (96–183)* | 130 (95–176)* | <0.01 |
| Total cholesterol (mg/dL) | 193.2 (36.3) | 198.6 (35.6) | 189.7 (36.8)† | 0.03 |
| CSVD markers (≥1) | 165 (21.6) | 43 (21.8) | 99 (32.9) | <0.01 |
| Presence of moderate to severe WMH | 73 (9.6) | 19 (9.6) | 60 (19.9) | <0.01 |
| Presence of microbleeds | 74 (9.71) | 18 (9.1) | 37 (12.3) | 0.30 |
| Presence of lacunes | 64 (8.4) | 12 (6.1) | 37 (12.3) | 0.041 |
Data are presented as mean (SD) or number (percentage).
NAFLD nonalcoholic fatty liver disease; NFS NAFLD fibrosis score, BMI body mass index, HDL high-density lipoprotein, MMSE Mini-Mental State Examination, CSVD cerebral small vessel disease, WMHs white matter hyperintensitie.
*P < 0.05 compared to no NAFLD; †P < 0.05 compared to FIB-4 < 1.45.
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).
Table 3.
Adjusted odds ratios (95% CI) for presence of moderate to severe white matter hyperintensities, lacunes, and microbleeds by nonalcoholic fatty liver disease (NAFLD) severity (n = 1,260).
| None (n = 762) | NAFLD (n = 498) | p value | NAFLD with FIB-4 < 1.45 (n = 259) | NAFLD with FIB-4 ≥ 1.45 (n = 239) | p for trend | |
|---|---|---|---|---|---|---|
| WMH | ||||||
| Crude model | reference | 1.78 (1.27, 2.50) | 0.001 | 1.45 (0.92, 2.20) | 2.19 (1.46, 3.28) | <0.001 |
| Model 1 | reference | 2.07 (1.44, 2.96) | <0.001 | 1.45 (0.94, 2.23) | 2.44 (1.61, 3.69) | <0.001 |
| Model 2 | reference | 1.64 (1.10, 2.42) | 0.014 | 1.14 (0.72, 1.82) | 1.77 (1.13, 2.78) | 0.016 |
| Lacunes | ||||||
| Crude model | reference | 1.19 (0.81, 1.76) | 0.38 | 0.86 (0.51, 1.47) | 1.57 (0.99, 2.48) | 0.11 |
| Model 1 | reference | 1.19 (0.80, 1.77) | 0.39 | 0.86 (0.50, 1.46) | 1.48 (0.92, 2.36) | 0.18 |
| Model 2 | reference | 0.99 (0.93, 1.54) | 0.95 | 0.69 (0.39, 1.22) | 1.14 (0.68, 1.88) | 0.79 |
| Microbleeds | ||||||
| Crude model | reference | 1.15 (0.80, 1.67) | 0.45 | 0.95 (0.59, 1.54) | 1.39 (0.89, 2.17) | 0.22 |
| Model 1 | reference | 1.16 (0.80, 1.69) | 0.43 | 0.95 (0.58, 1.53) | 1.34 (0.85, 2.11) | 0.28 |
| Model 2 | reference | 1.01 (0.66, 1.55) | 0.95 | 0.78 (0.47, 1.31) | 1.11 (0.68, 1.81) | 0.83 |
FIB-4 Fibrosis-4 index*; WMH white matter hyperintensities.
Model 1: Adjusted for age and sex.
Model 2: Further adjusted for smoking (never vs. past or current smokers), alcohol consumption (none vs. moderate), obesity (not obese vs. obese), hypertension, diabetes, and hyperlipidemia.
*For the FIB-4, the model was not adjusted for age, as this factor is included in the calculation of the FIB-4.
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).
Table 4.
Adjusted odds ratios (95% CI) for presence of moderate to severe white matter hyperintensity, lacunes, and microbleeds by non-alcoholic fatty liver disease (NAFLD) severity (n = 1,260).
| None (n = 762) | NAFLD (n = 498) | p value | NAFLD with NFS < −1.455 (n = 197) | NAFLD with NFS ≥ −1.455 (n = 301) | p for trend | |
|---|---|---|---|---|---|---|
| WMH | ||||||
| Crude model | reference | 1.78 (1.27, 2.50) | 0.001 | 1.01 (0.59, 1.71) | 2.35 (1.62, 3.41) | <0.001 |
| Model 1 | reference | 2.07 (1.44, 2.96) | <0.001 | 1.01 (0.59, 1.72) | 2.61 (1.78, 3.82) | <0.001 |
| Model 2 | reference | 1.64 (1.10, 2.42) | 0.014 | 0.92 (0.53, 1.59) | 2.05 (1.34, 3.14) | 0.002 |
| Lacunes | ||||||
| Crude model | reference | 1.19 (0.81, 1.76) | 0.38 | 0.71 (0.37, 1.34) | 1.53 (1.00, 2.35) | 0.09 |
| Model 1 | reference | 1.19 (0.80, 1.77) | 0.39 | 0.71 (0.37, 1.34) | 1.45 (0.94, 2.24) | 0.15 |
| Model 2 | reference | 0.99 (0.93, 1.54) | 0.95 | 0.63 (0.33, 1.22) | 1.20 (0.74, 1.95) | 0.61 |
| Microbleeds | ||||||
| Crude model | reference | 1.15 (0.80, 1.67) | 0.45 | 0.93 (0.54, 1.61) | 1.30 (0.86, 1.98) | 0.26 |
| Model 1 | reference | 1.16 (0.80, 1.69) | 0.43 | 0.93 (0.54, 1.61) | 1.27 (0.83, 1.94) | 0.33 |
| Model 2 | reference | 1.01 (0.66, 1.55) | 0.95 | 0.86 (0.50, 1.49) | 1.09 (0.68, 1.75) | 0.80 |
NFS NAFLD (Non-Alcoholic Fatty Liver Disease) Fibrosis Score*, WMH white matter hyperintensities.
Model 1: Adjusted for age and sex.
Model 2: Further adjusted for smoking (never vs. past or current smokers), alcohol consumption (none vs. moderate), obesity (not obese vs. obese), hypertension, diabetes, and hyperlipidemia.
*For the NFS, the model was not adjusted for age and diabetes. Because that variable already included in the NFS formula.
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).
Figure 1.
Odds ratios for prevalence of moderate or severe white matter hyperintensities by non-alcoholic fatty liver disease with FIB-4 ≥ 1.45 in predefined subgroups.
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).
Figure 2.
Flow chart of study participants.
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, College Station, TX, USA).
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 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.
Competing Interests
The authors declare no competing interests.
Footnotes
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Hyemin Jang, Danbee Kang, Juhee Cho and Sang Won Seo contributed equally.
Change history
10/10/2019
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Contributor Information
Juhee Cho, Email: alfadur2j@gmail.com.
Sang Won Seo, Email: sangwonseo@empal.com.
References
- 1.Chitturi S, Wong VW, Farrell G. Nonalcoholic fatty liver in Asia: Firmly entrenched and rapidly gaining ground. J Gastroenterol Hepatol. 2011;26(Suppl 1):163–172. doi: 10.1111/j.1440-1746.2010.06548.x. [DOI] [PubMed] [Google Scholar]
- 2.Chalasani N, et al. The diagnosis and management of non-alcoholic fatty liver disease: practice Guideline by the American Association for the Study of Liver Diseases, American College of Gastroenterology, and the American Gastroenterological Association. Hepatology. 2012;55:2005–2023. doi: 10.1002/hep.25762. [DOI] [PubMed] [Google Scholar]
- 3.Byrne CD, Targher G. NAFLD: a multisystem disease. J Hepatol. 2015;62:S47–64. doi: 10.1016/j.jhep.2014.12.012. [DOI] [PubMed] [Google Scholar]
- 4.Seo SW, et al. Nonalcoholic fatty liver disease is associated with cognitive function in adults. Neurology. 2016;86:1136–1142. doi: 10.1212/wnl.0000000000002498. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Chang Y, et al. Cohort study of non-alcoholic fatty liver disease, NAFLD fibrosis score, and the risk of incident diabetes in a Korean population. Am J Gastroenterol. 2013;108:1861–1868. doi: 10.1038/ajg.2013.349. [DOI] [PubMed] [Google Scholar]
- 6.Ryoo JH, et al. The clinical availability of non alcoholic fatty liver disease as an early predictor of the metabolic syndrome in Korean men: 5-year’s prospective cohort study. Atherosclerosis. 2013;227:398–403. doi: 10.1016/j.atherosclerosis.2013.01.002. [DOI] [PubMed] [Google Scholar]
- 7.Lonardo A, Ballestri S, Marchesini G, Angulo P, Loria P. Nonalcoholic fatty liver disease: a precursor of the metabolic syndrome. Dig Liver Dis. 2015;47:181–190. doi: 10.1016/j.dld.2014.09.020. [DOI] [PubMed] [Google Scholar]
- 8.Petta S, et al. The Presence of White Matter Lesions Is Associated With the Fibrosis Severity of Nonalcoholic Fatty Liver Disease. Medicine (Baltimore) 2016;95:e3446. doi: 10.1097/md.0000000000003446. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Knottnerus IL, Ten Cate H, Lodder J, Kessels F, van Oostenbrugge RJ. Endothelial dysfunction in lacunar stroke: a systematic review. Cerebrovasc Dis. 2009;27:519–526. doi: 10.1159/000212672. [DOI] [PubMed] [Google Scholar]
- 10.Sinn DH, et al. Ultrasonographically detected non-alcoholic fatty liver disease is an independent predictor for identifying patients with insulin resistance in non-obese, non-diabetic middle-aged Asian adults. Am J Gastroenterol. 2012;107:561–567. doi: 10.1038/ajg.2011.400. [DOI] [PubMed] [Google Scholar]
- 11.Thomas GN, et al. Deleterious impact of “high normal” glucose levels and other metabolic syndrome components on arterial endothelial function and intima-media thickness in apparently healthy Chinese subjects: the CATHAY study. Arterioscl Throm Vas. 2004;24:739–743. doi: 10.1161/01.ATV.0000118015.26978.07. [DOI] [PubMed] [Google Scholar]
- 12.Hamann GF, Okada Y, Fitridge R, del Zoppo GJ. Microvascular basal lamina antigens disappear during cerebral ischemia and reperfusion. Stroke. 1995;26:2120–2126. doi: 10.1161/01.STR.26.11.2120. [DOI] [PubMed] [Google Scholar]
- 13.Sam K, et al. Development of White Matter Hyperintensity Is Preceded by Reduced Cerebrovascular Reactivity. Ann Neurol. 2016;80:277–285. doi: 10.1002/ana.24712. [DOI] [PubMed] [Google Scholar]
- 14.Targher G, et al. Relations between carotid artery wall thickness and liver histology in subjects with nonalcoholic fatty liver disease. Diabetes Care. 2006;29:1325–1330. doi: 10.2337/dc06-0135. [DOI] [PubMed] [Google Scholar]
- 15.Winkleby MA, Jatulis DE, Frank E, Fortmann SP. Socioeconomic-Status and Health - How Education, Income, and Occupation Contribute to Risk-Factors for Cardiovascular-Disease. Am J Public Health. 1992;82:816–820. doi: 10.2105/Ajph.82.6.816. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Smith GD, Hart C, Blane D, Gillis C, Hawthorne V. Lifetime socioeconomic position and mortality: prospective observational study. BMJ. 1997;314:547–552. doi: 10.1136/bmj.314.7080.547. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Kwak MS, et al. Non-obese fatty liver disease is associated with lacunar infarct. Liver international: official journal of the International Association for the Study of the Liver. 2018;38:1292–1299. doi: 10.1111/liv.13663. [DOI] [PubMed] [Google Scholar]
- 18.Pantoni L. Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges. The Lancet. Neurology. 2010;9:689–701. doi: 10.1016/S1474-4422(10)70104-6. [DOI] [PubMed] [Google Scholar]
- 19.Farrall AJ, Wardlaw JM. Blood-brain barrier: ageing and microvascular disease–systematic review and meta-analysis. Neurobiol Aging. 2009;30:337–352. doi: 10.1016/j.neurobiolaging.2007.07.015. [DOI] [PubMed] [Google Scholar]
- 20.Fazekas F, et al. Histopathologic analysis of foci of signal loss on gradient-echo T2*-weighted MR images in patients with spontaneous intracerebral hemorrhage: evidence of microangiopathy-related microbleeds. AJNR Am J Neuroradiol. 1999;20:637–642. [PMC free article] [PubMed] [Google Scholar]
- 21.Korean Association for the Study of the Liver KASL clinical practice guidelines: management of nonalcoholic fatty liver disease. Clin Mol Hepatol. 2013;19:325–348. doi: 10.3350/cmh.2013.19.4.325. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Hernaez R, et al. Diagnostic accuracy and reliability of ultrasonography for the detection of fatty liver: a meta-analysis. Hepatology. 2011;54:1082–1090. doi: 10.1002/hep.24452. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Saverymuttu SH, Joseph AE, Maxwell JD. Ultrasound scanning in the detection of hepatic fibrosis and steatosis. British medical journal. 1986;292:13–15. doi: 10.1136/bmj.292.6512.13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Mathiesen UL, et al. Increased liver echogenicity at ultrasound examination reflects degree of steatosis but not of fibrosis in asymptomatic patients with mild/moderate abnormalities of liver transaminases. Digestive and liver disease: official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver. 2002;34:516–522. doi: 10.1016/S1590-8658(02)80111-6. [DOI] [PubMed] [Google Scholar]
- 25.Shah AG, et al. Comparison of noninvasive markers of fibrosis in patients with nonalcoholic fatty liver disease. Clinical gastroenterology and hepatology: the official clinical practice journal of the American Gastroenterological Association. 2009;7:1104–1112. doi: 10.1016/j.cgh.2009.05.033. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Angulo P, et al. The NAFLD fibrosis score: a noninvasive system that identifies liver fibrosis in patients with NAFLD. Hepatology. 2007;45:846–854. doi: 10.1002/hep.21496. [DOI] [PubMed] [Google Scholar]
- 27.Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA. MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. AJR Am J Roentgenol. 1987;149:351–356. doi: 10.2214/ajr.149.2.351. [DOI] [PubMed] [Google Scholar]
- 28.Noh Y, et al. A new classification system for ischemia using a combination of deep and periventricular white matter hyperintensities. Journal of stroke and cerebrovascular diseases: the official journal of National Stroke Association. 2014;23:636–642. doi: 10.1016/j.jstrokecerebrovasdis.2013.06.002. [DOI] [PubMed] [Google Scholar]
- 29.Wardlaw JM, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet. Neurology. 2013;12:822–838. doi: 10.1016/S1474-4422(13)70124-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III) Jama. 2001;285:2486–2497. doi: 10.1001/jama.285.19.2486. [DOI] [PubMed] [Google Scholar]
- 31.Pan W-H, Yeh W-T. How to define obesity? Evidence-based multiple action points for public awareness, screening, and treatment: an extension of Asian-Pacific recommendations. 2008;17:370–374. [PubMed] [Google Scholar]
- 32.Jones MP. Indicator and stratification methods for missing explanatory variables in multiple linear regression. J Am Stat Assoc. 1996;91:222–230. doi: 10.2307/2291399. [DOI] [Google Scholar]