v
Search
Advanced

Publications > Journals > Journal of Clinical and Translational Hepatology > Article Full Text

  • Original Article
  • OPEN ACCESS

Association between Metabolic Dysfunction-associated Fatty Liver Disease and Cognitive Impairment

  • Qiang Yu1,#,
  • Ruixin He2,3,#,
  • Haowen Jiang4,
  • Ji Wu1,
  • Zhifeng Xi1,
  • Kang He1,
  • Yongbo Liu1,
  • Tao Zhou1,
  • Mingxuan Feng1,
  • Ping Wan1,
  • Hexin Yan5,*  and
  • Qiang Xia1,* 
 Author information  Cite
Journal of Clinical and Translational Hepatology   2022;10(6):1034-1041

doi: 10.14218/JCTH.2021.00490

Abstract

Background and Aims

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a newly proposed term based on modified criteria. Although nonalcoholic fatty liver disease (NAFLD) has been well-documented as a multisystem disease, research on the correlation of MAFLD and extra-hepatic diseases is limited. This study aimed to clarify the association of MAFLD, as well as NAFLD status with cognitive function.

Methods

A total of 5,662 participants 20–59 years of age who underwent cognitive tests and liver ultrasonography in the Third National Health and Nutrition Examination Survey were included in the analysis. Cognitive function was evaluated using three computer-administered tests, the serial digit learning test (SDLT), the simple reaction time test (SRTT) and the symbol digit substitution test (SDST).

Results

Participants with MAFLD had significantly poorer performance on the SRTT [odds ratio (OR) 1.47, 95% confidence interval (CI): 1.14–1.89)]. MAFLD with moderate-severe liver steatosis was associated with higher risks of scoring low in the SDLT (OR 1.37, 95% CI: 1.04–1.82) and SRTT (OR 1.55, 95% CI: 1.19–2.02). NAFLD combined with metabolic dysfunction, instead of NAFLD without metabolic disorders, was associated an increased risk of a low SRTT score (OR 1.44, 95% CI: 1.10–1.82). MAFLD patients had a high probability of fibrosis, prediabetes, and diabetes and were also significantly associated with increased risks based on the SDST or SRTT score.

Conclusions

MAFLD was significantly associated with increased risk of cognitive impairment, especially among MAFLD patients with a high degree of liver fibrosis, moderate-severe steatosis, or hyperglycemia.

Keywords

NHANES, Metabolic dysfunction-associated fatty liver disease, Nonalcoholic fatty liver disease, Cognitive dysfunction

Introduction

Nonalcoholic fatty liver disease (NAFLD), characterized by liver steatosis with exclusion of excess alcohol intake and other secondary causes of liver disease, is one of the most common causes of all kinds of chronic liver diseases worldwide,1,2 affecting approximately 30% of adults.3 Accumulating evidence links NAFLD with extrahepatic diseases, such as cardiovascular disease,4 diabetes,5,6 and stroke.7 Based on the definition of NAFLD, an international panel of hepatologists from 22 countries proposed metabolic dysfunction-associated fatty liver disease (MAFLD) as a new term in 2019 to emphasize the indispensable role of metabolic dysfunction in causes, progression, and outcome of fatty liver disease.8 The pathogenesis of MAFLD involves a multitude of interlinked processes, such as insulin resistance, lipotoxicity, and infiltration of proinflammatory cells,9 which increase the risk of extrahepatic complications, including chronic kidney disease10 and cardiovascular diseases.11 Recent studies indicated that the definition of MAFLD was more appropriate than NAFLD for assessment of risks of liver disease progression,12 but whether MAFLD definition was superior to the old definition with regard to evaluation of extra-hepatic disease risks remains unclear.

The number of people living with dementia worldwide in 2015 was estimated at 47.47 million, and is expected to reach 135.46 million by 2050.13 Observational studies reported that NAFLD patients exhibited impaired executive function, abstract reasoning, and global cognitive function, especially those with high risks of hepatic fibrosis.14,15 This might be attributed to systemic inflammation, vascular dysfunction, and impaired urea cycle function related to NAFLD.14 However, evidence regarding the risks of cognitive impairment in key domains associated with MAFLD status has never been reported. Furthermore, whether the severity of liver steatosis, the probability of fibrosis or glucose metabolic status could modify the associations between MAFLD and cognitive function warrants explorations.

Therefore, taking advantage of the National Health and Nutrition Examination Survey (NHANES), a nationally representative sample of adults in the USA, we investigated the correlations of MAFLD status with cognitive functions in key domains and whether they were driven by the degree of liver fibrosis, liver steatosis, and hyperglycemia.

Methods

Study population

NHANES is an ongoing national cross-sectional survey to assess nutritional and health status of civilians in the USA. The survey is performed by the National Center for Health Statistics (https://www.cdc.gov/nchs/nhanes/index.htm ) and has a complex, multistage, stratified sampling design that provides nationally representative data. A group of 5,662 participants 20–59 years of age who underwent cognitive tests in the Third National Health and Nutrition Examination Survey (NHANES-III) were included in this study. The study sample was selected from households in 81 counties across the USA. The survey period was 1988–1994, and consisted of two phases of equal length and sample size. Both Phase 1 (conducted in 1988–1991) and Phase 2 (conducted in 1991–1994) were random samples of a population living in households in the USA.16 As summarized in Figure 1, participants with missing data of hepatic steatosis assessment (n=266) or cognitive tests (n=423) were excluded. As a consequence, 4,973 samples were included in the final analysis. The National Center for Health Statistics ethics review board reviewed and approved the survey and the NHANES participants gave informed consent prior to enrolment.

Flow chart of NHANES III participant selection.
Fig. 1  Flow chart of NHANES III participant selection.

MAFLD, metabolic dysfunction-associated fatty liver disease; NAFLD, nonalcoholic fatty liver disease; NHANES III, third National Health and Nutrition Examination Survey; T2DM, type 2 diabetes mellitus.

Data collection and definition

Demographic statistics and clinical data were collected in the in-home interviews by experienced interviewers. Categorical variables included sex (male and female), ethnicity (non-Hispanic, Mexican-American, other Hispanic), education (less than ninth grade and more than ninth grade), and history of stroke (yes and no).

Plasma glucose, serum low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG) were measured with a Hitachi 704 Analyzer (Boehringer Mannheim Diagnostics, Indianapolis, IN, USA). Alanine aminotransferase (ALT), aspartate aminotransferase (AST) were measured with a Hitachi 737 Analyzer (Boehringer Mannheim Diagnostics).17 In this study, the presence of diabetes was defined as fasting plasma glucose (FPG) ≥ 7.0 mmol/L, 2 h post-load plasma glucose (2 h PG) ≥11.1 mmol/L, glycated hemoglobin (HbA1c) ≥6.5%, or under oral hypoglycemic agent or insulin treatment.18 Prediabetes was defined as a FPG of 5.6–6.9 mmol/L, 2 h plasma glucose 7.8–11 mmol/L or HbA1c 5.7–6.4%.19 The homeostatic model assessment of insulin resistance (HOMA-IR) was calculated as fasting glucose (mmol/L) × plasma insulin (µIU/mL)/22.5 and used as surrogate measurement of insulin resistance.20

Height, weight, waist circumference (WC), and blood pressure (BP) were measured by trained examiners. BP was tested after resting for more than 5 m. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were tested three consecutive times and the average was used in the analysis. A body mass index (BMI, kg/m2) of >25 kg/m2 was considered overweight and >30 kg/m was considered obese.2,21

Cognitive function

Three computerized tests from the Neurobehavioral Evaluation System 2 were used to evaluate cognitive function, the serial digit learning test (SDLT), the simple reaction time test (SRTT) and the symbol digit substitution test (SDST).22,23 All cognitive tests were administered by trained examiners in either English or Spanish. The SDLT is a comprehensive measurement of learning ability, short-term memory, and concentration. Participants were shown sequences of eight Arabic numbers and were asked to type the entire sequence on the keyboard in the order in which it was presented. The test was over when participant completed a total of eight trials or correctly completed two consecutive trials, with the final output scored as the sum of errors.22–24 The SRTT evaluates visual-motor speed and response time. Participants were asked to press a button as soon as they could when they saw a square in the screen. The test was repeated 50 times, and the average reaction time of the last 40 trials was calculated and reported as the SRTT score. Reaction times of <50 milliseconds or >750 milliseconds and trials repeated <20 times were invalid according to the NHANES III instructions.24 The SDST assesses processing speed and visual attention. In this test, the digits one to nine were matched to different symbols. Participants were required to match the numbers and the symbols as quickly as possible. Four trials were conducted with different pairings of digits and symbols. The mean seconds per correct digit for each trial was calculated as the SDST result.22–24 Higher scores indicated worse cognitive performance. Cognitive impairment was defined as the highest quartile of test scores (SDLT ≥6, SRTT ≥247 and SDST ≥13).

MAFLD and NAFLD definitions

Hepatic ultrasonography was performed with a Toshiba SSA-90 A (Tustin, CA) machine using a 3.75 and 5.0 MHz transduces.25 Degree of hepatic steatosis was determined with a standard algorithm as none, mild, moderate, and severe.25 NAFLD was defined as hepatic steatosis detected by ultrasound in the absence of alcohol abuse (≥2 drinks per day for men and ≥1 drink per day for women),26,27 hepatitis B and hepatitis C.28,29 The diagnostic criteria of MAFLD30 included hepatic steatosis in addition to more than one of the following three criteria: overweight or obesity, type 2 diabetes mellitus (T2DM), and metabolic dysregulation. Metabolic dysregulation met more than two of (1) waist circumference (WC) ≥102 cm for men or 88 cm for women, (2) SBP ≥130 or DBP ≥85 mmHg or using antihypertensive medications; (3) plasma triglycerides ≥1.7 mmol/L or prescribed antilipemic medications, (4) plasma HDL-C <1 mmol/L or <1.3 mmol/L for men or women or used of prescribed medications, (5) prediabetes, (6) HOMA-IR ≥2.5, or (7) plasma high-sensitivity C-reactive protein level >2 mg/L.

Non-metabolic dysfunction NAFLD (non-MD-NAFLD) referred to participants who were diagnosed with NAFLD but did not meet the MAFLD criteria. NAFLD with metabolic dysfunction (MD) was defined as those participants who meet the criteria of both NAFLD and MAFLD.12 The presence of liver fibrosis was estimated by the NAFLD fibrosis score (NFS),31 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). A NFS ≤1.455 indicated a low probability of liver fibrosis; as NFS >1.455 indicated a higher probability of liver fibrosis.

Statistical analysis

Statistical analysis was performed using STATA 11.0, R 4.0.3 and took into consideration the complex sampling design in NHANES, including calculation of baseline weight, adjustment for nonresponse and over sampling of target populations. Weighted means with standard deviations of normally distributed variables, medians with 25th–75th percentiles of skewed distributed variables and percentages with 95% confidence intervals (CIs) of categorical data. We performed t-tests for continuous data and design-based χ2 tests for categorical data to determine statistical difference between characteristics of participants with normal and low scores in each cognitive test. ALT, AST, and HOMA-IR were log-transformed before statistical comparisons owing to their nonnormal, positively-skewed distributions. To evaluate the associations of MAFLD or NAFLD status with the risks of cognitive impairment, we conducted multivariate logistic regression analysis. Model 1 was unadjusted, and model 2 was adjusted for sex, age, ethnicity, education level, and history of stroke.

Results

Population characteristics

Table 1 shows the characteristics of the 4,973 participants by MAFLD and NAFLD status. Participants with MAFLD or NAFLD were significantly older. Differences were also observed in constituent ratios of race and educational level between MAFLD participants and non-MAFLD participants, and between NAFLD participants and non-NAFLD participants. As for their physical conditions, participants with MAFLD or NAFLD were more likely to be overweight, have diabetes or prediabetes, hypertension, higher WC, lower HDL-C level, and more severe insulin resistance.

Table 1

Characteristics of the study population by MAFLD and NAFLD status

MAFLDNon-MAFLDpNAFLDNon-NAFLDp
Patients, n1,3573,6161,6403,333
Weighted percentage, %23.476.629.970.1
Age, years41.26±10.9435.86±10.30<0.00139.10±11.1136.28±10.43<0.001
Male, %56.3 (51.9–60.8)47.2 (45.4–49.1)<0.00152.1 (47.8–56.3)47.9 (43.7–52.2)0.154
Ethnicity
  Non-Hispanic, %86.2 (83.0–89.4)91.5 (89.8–93.2)0.00287.5 (84.8–90.2)91.5 (89.8–93.2)0.009
  Mexican-American, %9.1 (6.7–11.4)4.8 (3.8–5.9)8.0 (5.6–10.2)4.9 (2.5–4.7)
  Other Hispanic, %4.7 (2.4–7.1)3.6 (2.5–4.7)4.5 (25.7–6.5)3.6 (2.5–4.7)
Education level
  <9 years, %14.79.80.00212.310.10.050
History of stroke, %0.510.740.580.660.520.72
BMI, kg/m231.33±7.1725.0±4.63<0.00128.96±7.5925.46±4.75<0.001
Diabetes, %13.0 (9.5–16.6)3.1 (2.3–4.0)<0.0019.9 (6.9–12.8)3.6 (2.6–4.5)<0.001
Prediabetes, %44.0 (38.2–49.8)19.5 (16.7–22.0)<0.00134.9 (30.2–39.6)21.1 (18.4–23.8)<0.001
Hypertension, %43.0 (37.5–48.5)17.9 (15.9–20.0)<0.00133.1 (28.1–38.0)19.8 (17.5–22.1)<0.001
Hyperlipemia, %38.1 (31.5–44.8)14.8 (11.6–17.9)<0.00129.7 (24.1–35.2)16.2 (12.8–19.6)<0.001
Waist circumference, cm103.4±16.286.8±12.2<0.00196.95±18.6188.06±12.33<0.001
HDL-C, mmol/L1.16±0.381.35±0.37<0.0011.23±0.411.34±0.38<0.001
HOMA-IR3.15 (2.15–5.08)1.61 (1.19–2.38)<0.0012.79 (1.83–4.78)1.66 (1.23–2.50)<0.001
AST, IU/L21 (17–26)18 (16–22)<0.00120 (17–25)18 (16–22)<0.001
ALT, IU/L20 (14–29)21 (17–26)<0.00118 (12–27)14 (10–19)<0.001
Albumin, g/dL41.69±4.0242.45±3.60<0.00141.99±3.6642.39±3.660.073
Platelet count, ×109/L277.32±76.97271.60±63.940.163275.13±75.26272.01±63.520.378
NFS >−1.455, %26.1 (21.7–30.2)12.4 (10.3–14.5)<0.00120.5 (17.2–23.8)13.5 (11.1–15.9)<0.001

Data are means ± standard deviation for normally distributed variables, median (25th–75th percentile) for variables with skewed distributions, and weighted percentages for categorical variables. t-tests for continuous data and design-based χ2 tests for categorical data. MAFLD vs. non-MAFLD or NAFLD or non-NAFLD. ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostasis model assessment-estimated insulin resistance MAFLD, metabolic dysfunction-associated fatty liver disease; NAFLD, nonalcoholic fatty liver disease; NFS, nonalcoholic fatty liver disease fibrosis score.

Risk of cognitive impairment according to MAFLD status

As shown in Table 2, significant associations between MAFLD and risk of cognitive impairment based on SDLT (OR 1.55, 95% CI: 1.22–1.98), SRTT (OR 1.55, 95% CI: 1.24–1.94), and SDST (OR 1.71, 95% CI: 1.36–2.15) were observed, and the associations remained robust for SRTT after adjusting for sex, age, ethnicity, education level, and history of stroke (OR 1.47, 95% CI: 1.14–1.89). It is worth mentioning that the odds of cognitive impairment assessed by SDLT (OR 1.37, 95% CI: 1.04–1.82) and SDST (OR 1.55, 95% CI: 1.19–2.02) increased significantly in MAFLD patients with moderate-severe liver steatosis, compared with those without hepatic steatosis even after adjusting for the aforementioned covariables.

Table 2

Risk of cognitive impairment by to MAFLD status

Model 1 (OR and 95% CI)pModel 2 (OR and 95% CI)p
SDLT ≥ 6 (Q4)
  No fatty liver (n=3,153)ReferenceReference
  MAFLD1.55 (1.22–1.98)0.0011.180.193
    MAFLD with mild hepatic steatosis (n=421)1.12 (0.81–1.54)0.4850.88 (0.62–1.26)0.487
    MAFLD with moderate-to-severe hepatic steatosis(n=867)1.81 (1.38–2.37)<0.0011.37 (1.04–1.82)*0.026
SRTT ≥ 247 (Q4)
  No fatty liver (n=3,253)ReferenceReference
  MAFLD (n=1,346)1.55 (1.24–1.94)<0.0011.47 (1.14–1.89)0.004
    MAFLD with mild hepatic steatosis (n=439)1.30 (0.87–1.96)0.1971.24 (0.83–1.87)0.289
    MAFLD with moderate-to-severe hepatic steatosis (n=907)1.64 (1.32–2.04)<0.0011.55 (1.19–2.02)0.002
SDST ≥13 (Q4)
  No fatty liver (n=3,223)ReferenceReference
  MAFLD (n=1,335)1.71 (1.36–2.15)<0.0011.06 (0.82–1.37)0.668
    MAFLD with mild hepatic steatosis (n=437)1.42 (1.03–1.96)0.0311.00 (0.68–1.47)0.990
    MAFLD with moderate-to-severe hepatic steatosis (n=898)1.80 (1.30–2.49)0.0011.10 (0.77–1.56)0.593

Model 1 was univariate; Model 2 adjusted for sex, age, ethnicity, education level, and history of stroke. CI, confidence interval; MAFLD, metabolic dysfunction-associated fatty liver disease; OR, odds ratio; Q4, fourth quartile; SDLT, the serial digit learning test; SDST, the symbol digit substitution test; SRTT, the simple reaction time test.

Risks of cognitive impairment by NAFLD status

As shown in Table 3, NAFLD with MD was significantly associated with high risk of poor performance in the SDLT (OR 1.59, 95% CI: 1.24–2.03), SRTT (OR 1.49, 95% CI: 1.19–1.87), and SDST (OR 1.68, 95% CI: 1.34–2.10), but the fully adjusted risks models were only significant for SRTT (OR 1.44, 95% CI: 1.10–1.82).It is worth mentioning that, there was no significance difference between participants with NAFLD but without metabolic dysfunction and participants without fatty liver regarding the risks of cognitive impairment.

Table 3

Risks of cognitive impairment according to NAFLD status

Model 1 (OR and 95% CI)pModel 2 (OR and 95% CI)p
SDLT ≥ 6 (Q4)
  Non-NAFLD (n=3,168)ReferenceReference
  Non-MD-NAFLD (n=330)0.90 (0.61–1.31)0.5661.06 (0.71–1.58)0.756
  NAFLD with MD (n=1,233)1.59 (1.24–2.03)<0.0011.20 (0.95–1.57)0.119
SRTT ≥ 247 (Q4)
  Non-NAFLD (n=3,270)ReferenceReference
  Non-MD-NAFLD (n=342)0.78 (0.51–1.21)0.2680.79 (0.52–1.21)0.279
  NAFLD with MD (n=1,305)1.49 (1.19–1.87)0.0011.44 (1.10–1.82)0.008
SDST ≥13 (Q4)
  Non-NAFLD (n=3,240)ReferenceReference
  Non-MD-NAFLD (n=339)0.70 (0.44–1.10)0.1200.99 (0.58–1.69)0.98
  NAFLD with MD (n=1,293)1.68 (1.34–2.10)<0.0011.06 (0.82–1.37)0.634

Model 1 was univariate; Model 2 adjusted for sex, age, ethnicity, education level, and history of stroke. CI, confidence interval; MAFLD, metabolic dysfunction-associated fatty liver disease; NAFLD with MD, metabolic dysfunction-associated nonalcoholic fatty liver disease; non-MD-NAFLD, non-metabolic dysfunction-associated nonalcoholic fatty liver disease; OR, odds ratio; Q4, fourth quartile; SDLT, serial digit learning test; SDST, symbol digit substitution test; SRTT, the simple reaction time test.

Risk of cognitive impairment by probability of fibrosis in MAFLD participants

After adjusting for aforementioned covariables, we observed that MAFLD with high probability of fibrosis was associated with significantly higher risks of cognitive impairment as assessed by the SRTT (OR 1.81, 95% CI: 1.19–2.76) and SDST (OR 1.67, 95% CI: 1.17–2.37), compared with MAFLD and a low probability of fibrosis. (Fig. 2)

Risk of cognitive impairment by probability of fibrosis in participants with MAFLD.
Fig. 2  Risk of cognitive impairment by probability of fibrosis in participants with MAFLD.

Logistic regression models were used to generate odds ratios (ORs) and corresponding 95% confidence intervals (CIs) to estimate the risk of cognitive impairment adjusted for sex, age, ethnicity, education level, and history of stroke. MAFLD, metabolic dysfunction-associated fatty liver disease; Q4, fourth quartile; SDLT, the serial digit learning test; SDST, the symbol digit substitution test; SRTT, the simple reaction time test.

Risk of cognitive impairment by glucose metabolic status in MAFLD participants

Glucose metabolic status was categorized as normal, prediabetes, and diabetes. Compared with MAFLD patients with normal glycemia, patients with prediabetes had a two-fold risk of developing cognitive impairment based on the SDST score (OR 2.01, 95% CI: 1.37–2.96). Moreover, the ORs of impaired performance in the SDST was nearly tripled in MAFLD with diabetes, compared with MAFLD patients with normal glycemic metabolism (OR 2.87, 95% CI: 1.44–5.74, Fig. 3).

Risk of cognitive impairment according to glucose metabolic status among MAFLD participants.
Fig. 3  Risk of cognitive impairment according to glucose metabolic status among MAFLD participants.

Logistic regression models were used to generate odds ratios (ORs) and corresponding 95% confidence intervals (CIs) to estimate the risk of cognitive impairment, adjusted for sex, age, ethnicity, education level, and history of stroke; MAFLD, metabolic dysfunction-associated fatty liver disease; Q4, fourth quartile; SDLT, serial digit learning test; SDST, the symbol digit substitution test; SRTT, simple reaction time test.

Discussion

To the best of our knowledge, this is the first comprehensive study to determine the association of MAFLD with domain-specific cognitive function among US adults. MAFLD was associated with an increased risk of impaired visual-motor speed and response time evaluated by the SRTT. MAFLD with moderate-severe liver steatosis was also associated with poorer performance on the SDLT and SRTT tests assessing learning ability, short-term memory, and concentration. Moreover, among MAFLD patients, high probabilities of fibrosis and abnormal glycemic metabolism were associated with worse processing speed and visual attention based on SDST scores. NAFLD without metabolic dysfunction was not associated with cognitive impairment.

Studies addressing associations between NAFLD and cognitive function are limited. One study reported that NAFLD was not independently associated with cognitive function, whereas NAFLD patients with a high risk of liver fibrosis, had worse executive function and abstract reasoning than those with a low risk.8 Similarly, another cross-sectional study reported that NAFLD alone was not associated with cognition, but NAFLD combined with T2DM was significantly associated with impaired visuospatial function.32 The results are in line with our findings that only NAFLD with metabolic dysfunction, but non-MD-NAFLD, was significantly associated with cognitive impairment, whereas MAFLD was independently associated with cognitive function, indicating the key role of metabolic dysfunction in the outcomes of fatty liver diseases. Hence, our study supports the emphasis of the new MAFLD definition that metabolic dysfunction is the core element together with accumulation of liver fat.8

MAFLD has been shown to be correlated with various extrahepatic diseases, such as CKD10 and cardiovascular disease.33 Our study contributes new knowledge that MAFLD is correlated with neurodegenerative diseases, making it a multisystem disease that warrants further exploration and attention. In addition to the comparison between MAFLD and non-MAFLD individuals, current evidence indicates the effect of MAFLD severity on various health outcomes. For example, the severity of MAFLD evaluated by fibrosis scores had a graded association with the risk of CKD.10 A previous cross-sectional study reported that hepatic fibrosis was independently associated with coronary artery calcification in NAFLD patients.34 Inflammation was one of the most important process promoting the development of cognitive impairment.35,36 MAFLD accelerated cognitive decline through chronic inflammation caused by imbalance of cytokines and adipokines.37 Hepatic fibrosis and fat accumulation may have mediating roles.38,39

In the context of the previous findings, our study demonstrated that degrees of liver steatosis and probability of liver fibrosis was associated with cognition, stressing the important role of MAFLD severity as a strong risk factor for multiple health conditions. As diabetes is a key element in the pathogenesis of MAFLD,8 we extended the knowledge by adding glucose metabolic status as the stratified variable. We found that both prediabetes and diabetes were significantly associated with increased risk of impaired processing speed and attention in MAFLD patients. The findings highlighted the importance of monitoring and management of glycemic status, even at a relatively early stage of glucose metabolic abnormalities.

The primary strengths of the study are a nationally representative sample, comprehensive measurements of liver steatosis, liver fibrosis, and cognitive functions in multiple domains. However, the study does have some limitations. Firstly, owing to the cross-sectional design of our data, a causal relation cannot be established between MAFLD and cognitive function. Secondly, the study population was restricted to individuals between 20 and 59 years of age, so the external validity for elder populations is uncertain. Further, ultrasound and NFS scores are widely used noninvasive standard procedure. Nonetheless, liver biopsy (the gold standard technique) is an invasive procedure that is not well suited for large population studies. Moreover, since only participants of NHANES III, rather than other NHANES cycles, received both hepatic ultrasound and cognitive tests, we performed the analysis using data derived from NHANES III. However, NHANES III was conducted in 1988–1994 and the population characteristics may be different from that of current population. Lastly, although our study provided epidemiological evidence supporting the association between MAFLD and cognitive impairment, biomedical studies are needed to further explore the pathological mechanism underlying the association.

In conclusion, our nationwide cross-sectional study showed that MAFLD was associated with cognitive impairment, and the presence of advanced fibrosis and glucose abnormalities were linked with cognitive impairment in MAFLD patients, Awareness of the associations is important for clinicians, as liver-focused treatments may reduce the risk of extrahepatic complications. Moreover, our findings suggest that management of dementia risk factors and screening for cognitive impairment should be added into the routine management of patients with MAFLD.

Abbreviations

2 h PG: 

2 h post-load plasma glucose

ALT: 

alanine aminotransferase

AST: 

aspartate aminotransferase

BMI: 

body mass index

BP: 

blood pressure

CI: 

confidence interval

CKD: 

chronic kidney disease DBP, diastolic blood pressure

FPG: 

fasting plasma glucose

HbA1c: 

glycated hemoglobin

HDL-C: 

high-density lipoprotein cholesterol

HOMA-IR: 

homeostatic model assessment of insulin resistance

LDL-C: 

low-density lipoprotein cholesterol

MAFLD: 

metabolic dysfunction-associated fatty liver disease

MD: 

metabolic dysfunction

NAFLD: 

nonalcoholic fatty liver disease

NFS: 

NAFLD fibrosis score

NHANES: 

National Health and Nutrition Examination Survey

Non-MD-NAFLD: 

non-metabolic dysfunction NAFLD

OR: 

odds ratio

SBP: 

systolic blood pressure

SDLT: 

the serial digit learning test

SDST: 

the symbol digit substitution test

SRTT: 

the simple reaction time test

T2DM: 

type 2 diabetes mellitus

TC: 

total cholesterol

TG: 

triglycerides

WC: 

waist circumference

Declarations

Ethical statement

This study was carried out in accordance with the recommendations of the Institutional Review Board of the Center for Disease Control and Prevention. All subjects gave written informed consent in accordance with the Declaration of Helsinki.

Data sharing statement

The datasets presented in this study can be found in online repositories (https://wwwn.cdc.gov/nchs/nhanes/Nhanes3/datafiles.aspx#core).

Funding

This work was supported by National Natural Science Foundation of China (Grant No. 32000525) and Shanghai Municipal Science and Technology Major Project (Grant No. TM202101H011).

Conflict of interest

The authors have no conflict of interests related to this publication.

Authors’ contributions

Conception and design (QY, RH, HY, QX), data collecting (QY, RH, HJ, JW, ZX, KH, data analysis (QY, RH, HJ, JW), data interpretation (ZX, KH, YL, TZ, MF, PW, QX), manuscript drafting and revising (QY, RH, HJ, JW, ZX, KH, YL, TZ, MF, PW, HY, QX).

References

  1. Bellentani S. The epidemiology of non-alcoholic fatty liver disease. Liver Int 2017;37(Suppl 1):81-84 View Article PubMed/NCBI
  2. Fazel Y, Koenig AB, Sayiner M, Goodman ZD, Younossi ZM. Epidemiology and natural history of non-alcoholic fatty liver disease. Metabolism 2016;65(8):1017-1025 View Article PubMed/NCBI
  3. Xiao J, Wang F, Wong NK, He J, Zhang R, Sun R, et al. Global liver disease burdens and research trends: Analysis from a Chinese perspective. J Hepatol 2019;71(1):212-221 View Article PubMed/NCBI
  4. Hanafy AS, Seleem WM, El-Kalla F, AbdAlkhalik Basha M, Abd-Elsalam S. Efficacy of a non-invasive model in predicting the cardiovascular morbidity and histological severity in non-alcoholic fatty liver disease. Diabetes Metab Syndr 2019;13(3):2272-2278 View Article PubMed/NCBI
  5. Hamed AE, Elwan N, Naguib M, et al. Diabetes Association with Liver Diseases: An Overview for Clinicians. Endocr Metab Immune Disord Drug Targets 2019;19(3):274-280 View Article PubMed/NCBI
  6. Abdelmoemen G, Khodeir SA, Zaki AN, Kassab M, Abou-Saif S, Abd-Elsalam S. Overexpression of Hepassocin in Diabetic Patients with Nonalcoholic Fatty Liver Disease May Facilitate Increased Hepatic Lipid Accumulation. Endocr Metab Immune Disord Drug Targets 2019;19(2):185-188 View Article PubMed/NCBI
  7. Abdeldyem SM, Goda T, Khodeir SA, Abou Saif S, Abd-Elsalam S. Nonalcoholic fatty liver disease in patients with acute ischemic stroke is associated with more severe stroke and worse outcome. J Clin Lipidol 2017;11(4):915-919 View Article PubMed/NCBI
  8. Eslam M, Sanyal AJ, George  J, International Consensus Panel. MAFLD: A Consensus-Driven Proposed Nomenclature for Metabolic Associated Fatty Liver Disease. Gastroenterology 2020;158(7):1999-2014 View Article PubMed/NCBI
  9. Kuchay MS, Choudhary NS, Mishra SK. Pathophysiological mechanisms underlying MAFLD. Diabetes Metab Syndr 2020;14(6):1875-1887 View Article PubMed/NCBI
  10. Sun DQ, Jin Y, Wang TY, Zheng KI, Rios RS, Zhang HY, et al. MAFLD and risks of CKD. Metabolism 2021;115:154433 View Article PubMed/NCBI
  11. Lee H, Lee YH, Kim SU, Kim HC. Metabolic Dysfunction-Associated Fatty Liver Disease and Incident Cardiovascular Disease Risk: A Nationwide Cohort Study. Clin Gastroenterol Hepatol 2021;19(10):2138-2147.e10 View Article PubMed/NCBI
  12. Lin S, Huang J, Wang M, Kumar R, Liu Y, Liu S, et al. Comparison of MAFLD and NAFLD diagnostic criteria in real world. Liver Int 2020;40(9):2082-2089 View Article PubMed/NCBI
  13. London: Alzheimer’s Disease International. Policy Brief for Heads of Government: The Global Impact of Dementia 2013–2050. Available from: https://www.alzint.org/u/2020/08/GlobalImpactDementia2013.pdf
  14. Kjærgaard K, Mikkelsen ACD, Wernberg CW, Grønkjær LL, Eriksen PL, Damholdt MF, et al. Cognitive Dysfunction in Non-Alcoholic Fatty Liver Disease-Current Knowledge, Mechanisms and Perspectives. J Clin Med 2021;10(4):673 View Article PubMed/NCBI
  15. Weinstein G, Davis-Plourde K, Himali JJ, Zelber-Sagi S, Beiser AS, Seshadri S. Non-alcoholic fatty liver disease, liver fibrosis score and cognitive function in middle-aged adults: The Framingham Study. Liver Int 2019;39(9):1713-1721 View Article PubMed/NCBI
  16. Plan and Operation of the Third National Health and Nutrition Examination Survey, 1988-1994. Avaialbe from: https://www.cdc.gov/nchs/data/series/sr_01/sr01_032.pdf
  17. Gunter EW, Lewis BG, Koncikowski SM. Laboratory Procedures Used for the Third National Health and Nutrition Examination Survey (NHANES III), 1988-1994. Avilable from: https://wwwn.cdc.gov/nchs/data/nhanes3/manuals/labman.pdf
  18. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 2014;37(Suppl 1):S81-S90 View Article PubMed/NCBI
  19. American Diabetes Association. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2019. Diabetes Care 2019;42(Suppl 1):S13-S28 View Article PubMed/NCBI
  20. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28(7):412-419 View Article PubMed/NCBI
  21. Prospective Studies Collaboration, Whitlock G, Lewington S, Sherliker P, Clarke R, Emberson J, et al. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet 2009;373(9669):1083-1096 View Article PubMed/NCBI
  22. Letz R. The Neurobehavioral Evaluation System 2 User’s Manual Winchester, MA: Neurobehavioral Systems Inc. 1990
  23. Baker EL, Letz RE, Fidler AT, Shalat S, Plantamura D, Lyndon M. A computer-based neurobehavioral evaluation system for occupational and environmental epidemiology: methodology and validation studies. Neurobehav Toxicol Teratol 1985;7(4):369-377 PubMed/NCBI
  24. Krieg EF, Chrislip DW, Letz RE, Otto DA, Crespo CJ, Brightwell WS, et al. Neurobehavioral test performance in the third National Health and Nutrition Examination Survey. Neurotoxicol Teratol 2001;23(6):569-589 View Article PubMed/NCBI
  25. National Health and Nutrition Examination Survey (NHANES) III. Hepatic Steatosis Ultrasound Images Assessment Procedures Manual. Available from: https://wwwn.cdc.gov/nchs/data/nhanes3/manuals/hsuiaap.pdf
  26. Becker U, Deis A, Sørensen TI, Grønbaek M, Borch-Johnsen K, Müller CF, Schnohr P, et al. Prediction of risks of liver disease by alcohol intake, sex, and age: a prospective population study. Hepatology 1996;23(5):1025-1029 View Article PubMed/NCBI
  27. Bellentani S, Saccoccio G, Costa G, Tiribelli C, Manenti F, Sodde M, et al. Drinking habits as cofactors of risks for alcohol induced liver damage. The Dionysos Study Group. Gut 1997;41(6):845-850 View Article PubMed/NCBI
  28. European Association for the Study of the Liver (EASL); European Association for the Study of Diabetes (EASD); European Association for the Study of Obesity (EASO). EASL-EASD-EASO Clinical Practice Guidelines for the management of non-alcoholic fatty liver disease. Diabetologia 2016;59(6):1121-1140 View Article PubMed/NCBI
  29. Chalasani N, Younossi Z, Lavine JE, Charlton M, Cusi K, Rinella M, et al. The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases. Hepatology 2018;67(1):328-357 View Article PubMed/NCBI
  30. Eslam M, Newsome PN, Sarin SK, Anstee QM, Targher G, Romero-Gomez M, et al. A new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement. J Hepatol 2020;73(1):202-209 View Article PubMed/NCBI
  31. Xiao G, Zhu S, Xiao X, Yan L, Yang J, Wu G. Comparison of laboratory tests, ultrasound, or magnetic resonance elastography to detect fibrosis in patients with nonalcoholic fatty liver disease: A meta-analysis. Hepatology 2017;66(5):1486-1501 View Article PubMed/NCBI
  32. Weinstein AA, de Avila L, Paik J, Golabi P, Escheik C, Gerber L, et al. Cognitive Performance in Individuals With Non-Alcoholic Fatty Liver Disease and/or Type 2 Diabetes Mellitus. Psychosomatics 2018;59(6):567-574 View Article PubMed/NCBI
  33. Lee H, Lee YH, Kim SU, Kim HC. Metabolic Dysfunction-Associated Fatty Liver Disease and Incident Cardiovascular Disease Risks: A Nationwide Cohort Study. Clin Gastroenterol Hepatol 2020;19(10):2138-2147 View Article PubMed/NCBI
  34. You SC, Kim KJ, Kim SU, Kim BK, Park JY, Kim DY, et al. Hepatic fibrosis assessed using transient elastography independently associated with coronary artery calcification. J Gastroenterol Hepatol 2015;30(10):1536-1542 View Article PubMed/NCBI
  35. Rosenberg GA, Bjerke M, Wallin A. Multimodal markers of inflammation in the subcortical ischemic vascular disease type of vascular cognitive impairment. Stroke 2014;45(5):1531-1538 View Article PubMed/NCBI
  36. Takeda S, Sato N, Morishita R. Systemic inflammation, blood-brain barrier vulnerability and cognitive/non-cognitive symptoms in Alzheimer disease: relevance to pathogenesis and therapy. Front Aging Neurosci 2014;6:171 View Article PubMed/NCBI
  37. Marra F, Bertolani C. Adipokines in liver diseases. Hepatology 2009;50(3):957-969 View Article PubMed/NCBI
  38. Van Gaal LF, Mertens IL, De Block CE. Mechanisms linking obesity with cardiovascular disease. Nature 2006;444(7121):875-880 View Article PubMed/NCBI
  39. Fargion S, Porzio M, Fracanzani AL. Nonalcoholic fatty liver disease and vascular disease: state-of-the-art. World J Gastroenterol 2014;20(37):13306-13324 View Article PubMed/NCBI
Copyright © 2022 Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 4.0 License (CC BY-NC 4.0), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • Journal of Clinical and Translational Hepatology
  • pISSN 2225-0719
  • eISSN 2310-8819

Article Options

Metrics

Cite this Article

  • © 2024 Xia & He Publishing Inc.
  • Total visits : 2762437
  • Visits today : 305
Back to Top

Association between Metabolic Dysfunction-associated Fatty Liver Disease and Cognitive Impairment

Qiang Yu, Ruixin He, Haowen Jiang, Ji Wu, Zhifeng Xi, Kang He, Yongbo Liu, Tao Zhou, Mingxuan Feng, Ping Wan, Hexin Yan, Qiang Xia
  • Reset Zoom
  • Download TIFF