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. Author manuscript; available in PMC: 2023 Dec 1.
Published in final edited form as: Neurobiol Aging. 2022 Aug 17;120:34–42. doi: 10.1016/j.neurobiolaging.2022.08.004

Association of Liver Disease with Brain Volume Loss, Cognitive Decline, and Plasma Neurodegenerative Disease Biomarkers

Zhongsheng Peng 1, Michael R Duggan 1, Heather E Dark 1, Gulzar N Daya 1, Yang An 1, Christos Davatzikos 2, Guray Erus 2, Alexandria Lewis 3, Abhay R Moghekar 3, Keenan A Walker 1,*
PMCID: PMC9685609  NIHMSID: NIHMS1836255  PMID: 36115133

Abstract

Although liver dysfunction has been implicated in Alzheimer’s disease (AD), it remains unknown how liver disease may influence the trajectory of brain and cognitive changes in older adults. We related self-reported liver disease to longitudinal measures of brain structure and cognition, as well as baseline measures of plasma AD/neurodegeneration biomarkers in the Baltimore Longitudinal Study of Aging. Liver disease was identified using ICD-9 classification codes. Brain volume and cognition were assessed serially using 3T-MRI and a cognitive battery. 1,008, 2,157, and 780 participants were included in the MRI, cognitive, and plasma biomarker analysis, respectively. After adjustment for confounders, liver disease was associated with accelerated decline in total brain and white matter volume, but not total gray matter or AD signature region volume. Although liver disease showed no relationship with domain-specific cognitive decline or plasma biomarkers, participants with a history of hepatitis demonstrated accelerated decline in verbal fluency and elevated neurofilament light. Results suggest all-cause liver disease may accelerate brain volume loss but does not appear to promote AD-specific neurocognitive changes.

Keywords: Alzheimer’s disease, liver disease, longitudinal study, MRI, cognitive assessment, AD plasma biomarkers

1. Introduction

Alzheimer’s disease (AD) is the most common form of dementia, influencing more than 40 million people globally (Patterson, 2018). AD is pathologically characterized by the emergence of amyloid-β (Aβ) plaques, tau neurofibrillary tangles, and neurodegeneration. Aβ generation is a conserved physiological process across multiple tissues, and the liver plays an important role in its metabolism, particularly the clearance of Aβ from circulating blood (Bassendine et al., 2020; Cheng et al., 2020). As a regulator of peripheral Aβ levels, a source of proinflammatory proteins, and as a central player in protein and lipid metabolism, liver health may influence the risk for cognitive decline and dementia among older adults (Estrada et al., 2019; Sutcliffe et al., 2011; Wu et al., 2021).

Like AD, chronic liver disease is a highly prevalent condition, affecting as many as 1.5 billion people worldwide (Asrani et al., 2019). Chronic liver diseases, such as liver cirrhosis, non-alcoholic fatty liver disease (NAFLD), and hepatitis C virus (HCV) infection have been associated with increased dementia risk in some (Chen et al., 2017; Chiu et al., 2014; Lampignano et al., 2021), but not all, studies (Labenz et al., 2021). Moreover, liver function markers, such as alanine aminotransferase (ALT), were recently associated with AD diagnosis, cognitive impairment, and abnormal AD biomarkers in CSF (Nho et al., 2019). Translational research has also supported a connection between liver disease and the mechanisms underlying dementia. For example, a murine model of NAFLD was found to accelerate pathological signs of AD, including Aβ plaque deposition, aggregation of tau neurofibrillary tangles, and cerebral amyloid angiopathy (CAA) (Kim et al., 2016).

Although liver disease has been associated with prevalent and incident dementia/AD dementia, the structural and molecular brain changes that underly the connection between liver disease and dementia remain poorly understood. NAFLD has been associated with smaller total brain, but not hippocampal, volume in the Framingham study (Weinstein et al., 2018), and non-cirrhotic HCV patients were found to have reduced cortical thickness (Hjerrild et al., 2016) and a greater degree of white matter microstructural abnormalities (Prell et al., 2019). However, these results are not consistent, as one recent cohort study found no association between chronic liver disease and baseline or longitudinal brain volume (Basu et al., 2021).

There is a need to understand the brain changes associated with liver disease, as these changes may play a role in increasing risk for subsequent cognitive decline or the development of dementia. To date, few studies have examined the association between liver disease and longitudinally measured neuroimaging or cognitive outcomes. As such, it remains unknown whether liver disease may constitute a risk factor for subsequent neurodegeneration and accelerated cognitive decline. By examining the association of liver dysfunction (defined agnostic to etiology) with longitudinal brain volume and cognitive changes, as well as plasma biomarkers of neurodegenerative disease, the current study provides a comprehensive examination of the chronic effects of non-specific liver disease on the aging brain and AD pathological processes using data from the Baltimore Longitudinal Study of Aging (BLSA). We hypothesized that liver disease, as defined by self-report and International Classification of Diseases, Ninth Revision (ICD-9) classification, would be associated with greater declines in brain volume and cognition, and with abnormalities in AD and neurodegeneration plasma biomarkers.

2. Methods

2.1. Participants and Study Design

The BLSA is an ongoing and continuously enrolling cohort study of normal human aging started in 1958 (Shock, 1984; Stone & Norris, 1966). Participant enrollment criteria and procedures have been previously described (Ferrucci, 2008; Resnick et al., 2003; Schrack et al., 2014). Historically, participants underwent neuropsychological testing and physical assessment biannually unless enrolled in a neuroimaging study where study visits occurred annually from 1994-2005. After 2005 study visits occurred every 1 to 4 years depending on age (age <60 years, every 4 years; age 60-79 years, every 2 years; age ≥80 years, every year). The study design and participant selection for this analysis are displayed in Figure 1. The BLSA protocol was approved by the Institutional Review Board of the National Institute of Environmental Health Science, National Institutes of Health (03AG0325). Informed consent was obtained from all participants, and deidentified BLSA data were used for analyses.

Figure 1.

Figure 1.

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Flow chart of Baltimore Longitudinal Study of Aging and participant exclusions.

a Participants underwent neuropsychological testing and physical assessment biannually unless enrolled in a neuroimaging study where study visits occurred annually from 1994-2005, after which study visits occurred every 1 to 4 years depending on age: age <60 years, every 4 years; age 60-79 years, every 2 years; age ≥80 years, every year. Participants with the following neurological disorders were excluded: stroke, Parkinson’s disease, head trauma, brain tumor, seizures, ataxia of gait, and paresis of extremities. Additionally, individuals with bipolar disorder were excluded.

Abbreviations: ICD9, International Classification of Diseases, Ninth Revision; MRI, Magnetic Resonance Imaging.

The analysis of brain volume, cognition, and plasma biomarkers included a partially overlapping sample of participants selected based on the availability of 3T MRI, cognitive, and biomarker data, respectively. For each analysis, participants were divided into two groups: participants with a history of liver disease (present) and participants without a history of liver disease (absent). Because hepatitis was the most common form of liver disease in our sample, we performed a secondary analysis of the subset of participants with hepatitis. For these analyses, participants with non-hepatitis liver disease were excluded. For participants with liver disease, the baseline MRI or cognitive assessment visit was the earliest visit at which the presence of liver disease was documented. MRI and cognitive data derived from any visit before this baseline visit was not considered. For participants without liver disease, the baseline visit for MRI or cognitive assessment was the earliest visit at which the absence of a liver disease diagnosis was documented.

2.2. Liver Disease Diagnoses

Beginning as early as 1960, participants received a comprehensive health and functional screening evaluation by a licensed health care professional (e.g., nurse practitioner, study physician) at each study visit. As part of these evaluations, information on participant health was collected via participant interview and completion of a medical questionnaire relying primarily on self-report of physician diagnosis. Medical diagnoses were extracted from these interviews/questionnaires and were categorized using ICD-9 codes. Liver disease classification was based on ICD-9 codes corresponding to liver disease diagnoses, including hepatitis and other liver-related conditions, such as abnormal liver function, hepatomegaly, NAFLD and autoimmune liver disease. A list of ICD-9 codes used for this analysis is provided in Table 1. Liver cyst or benign liver tumor with normal liver function were not considered liver disease for this analysis. Participants with liver cirrhosis were excluded because this form of late-stage liver disease is known to have effects on the brain (e.g., hepatic encephalopathy) that are distinct from that associated with less severe forms of chronic liver disease – the focus of the current study (Chen et al., 2012; Iwasa et al., 2012). Notably, no cases of alcohol related liver damage (e.g., alcoholic hepatitis, alcoholic fatty liver) or liver cancer were identified in this study.

Table 1.

ICD-9 codes for selection and exclusion of liver disease

ICD_9 Disease diagnosis MRI (No.) Cognition (No.) Plasma biomarker (No.)
70 Viral hepatitis
70.1 Viral hepatitis A without mention of hepatic coma 7 14 6
70.3 Viral hepatitis B without mention of hepatic coma 6 17 3
70.42 Hepatitis delta without mention of active hepatitis B disease with hepatic coma
70.51 Hepatitis C virus 8 20 3
70.52 Hepatitis delta without mention of active hepatitis B disease or hepatic coma
70.9 Unspecified viral hepatitis without mention of hepatic coma 13 44 12
155 Liver, primary carcinoma, hepatoblastoma
571 Chronic liver disease and cirrhosis
571 Alcoholic fatty liver
571.1 Acute alcoholic hepatitis
571.2 Alcoholic cirrhosis of liver
571.3 Alcoholic liver damage, unspecified
571.4 chronic hepatitis excludes viral hepatitis 1 1 1
571.42 Autoimmune hepatitis
571.49 Other chronic hepatitis
571.8 Other chronic nonalcoholic liver disease 7 11 7
571.9 Unspecified chronic liver disease without mention of alcohol
573 Other disorders of liver
573.3 Hepatitis, unspecified 10 27 10
789.1 Hepatomegaly 3 29 3
790.2 Abnormal liver function tests 3 3 3
790.4 Elevated liver function tests 29 92 27
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2.3. Magnetic Resonance Image

Protocol for the BLSA brain MRI scan has been described previously in detail (June et al., 2020). Briefly, MRI scanning was performed on a 3-T Philips Achieva scanner. Repetition time [TR]=6.8 ms, echo time [TE]=3.2 ms, flip angle=8°, image matrix=256 × 256, 170 slices, pixel size=1 × 1 mm, and slice thickness=1.2 mm were applied for T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) scans. MUSE (Multi-atlas Region Segmentation Utilizing Ensembles) anatomic labeling method was used to generate an ensemble of labeled atlases in target image space by combining different atlases, warping algorithms, standardizing parameters, and using a consistent labeling approach to fuse these labels into a final segmentation (Doshi et al., 2016; Erus et al., 2018). The present volumetric analysis examined four primary regions of interest (ROIs): total brain, total white matter, total gray matter, and AD signature region volume (the combined volume of hippocampus, parahippocampal gyrus, entorhinal cortex, posterior cingulate gyrus, precuneus, and cuneus ROIs) as defined previously (Graff-Radford et al., 2017). In the case that liver disease was significantly associated with a primary ROI, we performed secondary analyses to examine lobar-specific associations. White matter lesion volume, measured by DeepMRSseg segmentation of white matter lesions on FLAIR scans, was also examined as a primary outcome (Doshi et al., 2019).

2.4. Cognitive Assessment

Cognition was assessed at each visit. Five cognitive domain scores (verbal memory, verbal fluency, executive function, attention, and visuospatial ability) were calculated and examined as outcome measures (Varadaraj et al., 2021). The verbal memory domain included the immediate free recall and long-delayed free recall total correct scores from the California Verbal Learning Test (CVLT) (Delis, 2000). The verbal fluency domain included total correct words on the semantic fluency and phonemic fluency measures (Buschke & Fuld, 1974). The executive function domain included the completion times of Trail-Making Test Part B and total score on the WAIS-R digit span backward measure. The attention domain included completion time of the Trail-Making Test Part A and total score on the WAIS-R digit span forward measure (Blackburn & Benton, 1957; Reitan, 1986). The visuospatial ability domain included score on the Card Rotation test and two Clock Drawing Tests (Rouleau et al., 1992; Wilson et al., 1975). All test scores were standardized to z scores based on the means and standard deviation (SD) of the baseline measures; composite scores for each visit were calculated by averaging the participants’ z scores of the cognitive tasks in each cognitive domain. Before calculation of composite scores, the completion times of the Trail Making Test Parts A and B were first natural log–transformed and the signs were inverted so that higher scores reflect better performance. Given that certain cognitive tasks were initiated in the BLSA at different periods according to protocol changes, composite scores for participants at each visit were computed from those tasks available at the time of clinical assessment.

The Petersen criteria (Petersen, 2004) was used to identify mild cognitive impairment (MCI). Dementia was diagnosed according to diagnostic and Statistical Manual, third edition, revised (DSM-III-R) (Association & Association, 2013) and the National Institute of Neurological and Communication Disorders and Stroke-Alzheimer’s Disease and Related Disorders Association criteria (McKhann et al., 1984).

2.5. Plasma Biomarker Assay

Blood for plasma biomarkers was collected at the time of the baseline 3T MRI. For a subset of participants, blood specimens were collected at the time of the first PET scan as part of a separate study. Plasma was separated, aliquoted and stored at −80°C using standardized protocols. Using EDTA plasma, Aβ42, Aβ40, glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL) and phosphorylated tau181 (pTau181) were measured on the Single Molecule Array (Simoa) HD-X instrument (Quanterix Corporation) using the Simoa Neurology 4-Plex E (N4PE) assay. Assays were run in duplicate. Intraassay coefficients of variation (CV) for Aβ42, Aβ40, GFAP, NfL, and pTau181 were 1.9, 2.8, 5.0, and 5.1, respectively. We used a threshold of mean ± 5SD to identify and winsorize outliers. GFAP, NfL and pTau181 were transformed to account for skewness.

2.6. Covariates

Participants completed a health history assessment and a physical examination at each visit. Age, sex, race, and total years of education were based on participant self-report. APOEε4 carrier status was measured using PCR plus restriction isotyping with the Type IIP enzyme Hhai or the Taqman method, as described previously (Shafer et al., 2021) and defined as 0 ε4 alleles, ≥1 ε4 alleles, or unknown. Estimated glomerular filtration rate (eGFR)-creatinine was calculated at the time of biomarker measurement using CKD-EPI criteria (Levey et al., 2009). The presence of chronic conditions after enrollment was assessed by nurse practitioners and established according to information on medical history, drug treatment, and physical examination. To account for chronic diseases, we used a comorbidity index calculated as the sum of eight conditions at baseline: hypertension, diabetes mellitus, obesity, ischemic heart disease, congestive heart failure, cancer, chronic obstructive pulmonary disease (COPD), and chronic kidney disease (CKD) (Gresham et al., 2018). A score based on the presence/absence (1=yes, 0=no) for each of these eight conditions for each participant was generated; this comorbidity index was then converted to a percentage to account for missing comorbidity data. A body mass index (BMI) >30kg/m2 and a HbA1c >6.5% were used defined as obesity and diabetes, respectively. Missing covariate information was imputed using information from the nearest visit with available data.

2.7. Statistical Analyses

Baseline participant characteristics were compared using t-tests for continuous variables and Chi-square or Fisher’s exact test for categorical variables. Linear mixed effects (LME) models were used to examine the association of liver disease (present/absent) with baseline and longitudinal MRI brain volumes, white matter lesions, and cognitive domain scores. In all models, time indicated follow-up time (in years) from the first MRI scan or the first cognitive test for each participant. We included the fixed effects of liver-disease or [hepatitis], time, and liver-disease or [hepatitis] by time interaction. Main effect of liver-disease or [hepatitis] estimated cross-sectional baseline relationships with brain volume/cognition and its interaction with time estimated effect of liver disease on longitudinal brain volume change and cognitive change. All models were adjusted for age (at baseline), sex (male/female), race (white/non-white), education, APOEε4, comorbidity index, and the interaction between each covariate and time. Additionally, we examined whether sex and APOEε4 status modified the association of liver-disease with brain volume and cognition by incorporating three-way interaction terms (liver-disease or [hepatitis]*moderator*time) and all the lower order terms in the model. For all MRI analyses, we additionally adjusted intracranial volume (ICV) defined at age 70. Random effects included intercept and time with unstructured covariance. All LME models were estimated using the restricted maximum likelihood (REML) method. We used independent samples t-tests to examine unadjusted differences in plasma biomarker levels between participants with vs. without liver disease. We additionally performed a linear regression to relate liver disease to plasma biomarkers adjusting for baseline age, sex, race, education, APOEε4, kidney function (eGFR-creatinine), and comorbidity index. We examined separate linear regression models incorporating a two-way interaction term to examine APOEε4 and sex as effect modifiers. Additionally, we conducted a series of sensitivity analyses which examined the effect of excluding participants with cognitive impairment (i.e., dementia, MCI, impaired but not MCI) at the baseline MRI, cognitive, and plasma biomarker visit. Analyses were performed in SAS 9.4 (Cary, NC). A significance level of p<0.05, uncorrected, was used for analyses.

3. Results

3.1. Liver Disease and Longitudinal Brain Volume Trajectories

The MRI analysis included 1,008 participants (baseline age 66 [SD=15]; 55% women; 34% non-White race), 87 (9%) of which had one or more diagnosis of liver disease. The most common liver disease was hepatitis (n=45). Ninety-eight percent of participants were cognitively normal at the baseline visit. The mean follow-up time was 3.4 years (median: 3.3; IQR: 0.0, 6.2) and mean number of repeated MRIs was 2.6 (median: 2.0; IQR: 1.0, 4.0). The mean time between liver disease diagnosis and baseline MRI was 18.0 years (median: 12.0; IQR: 4.0, 29.0). As displayed in Supplementary Table 1, participants with a history of liver disease had a higher prevalence of other chronic diseases.

Participants with liver disease did not significantly differ from participants without liver disease on measures of baseline brain volume (Table 2). However, the liver disease group demonstrated significantly steeper declines in total brain volume (β=−0.95; SE=0.41; p=0.02) and total white matter volume (β=−0.46; SE=0.18; p=0.01; Figure 2A, B; Supplementary Figure 1). This finding did not extend to gray matter volume, AD signature region volume, or white matter lesion volume. Next, we conducted secondary analysis of total lobar volume and total lobar white matter volume to understand regional specificity. Participants with liver disease demonstrated accelerated declines in frontal lobe volume, as well as frontal and temporal white matter volumes (Table 2). After restricting analyses to participants cognitively normal at the time of baseline MRI (Supplementary Table 2) and after adjusting for individual comorbid conditions (rather than a comorbidity index; Supplementary Table 3), we found similar results. Compared to participants without liver disease, the subgroup of participants with hepatitis did not demonstrate significant cross-sectional or longitudinal differences in brain volume (Supplementary Table 4). APOEε4 status and sex did not significantly modify the relationship between liver disease and brain volume loss.

Table 2.

Baseline and longitudinal association of liver disease with MRI-defined brain structure

Brain region of interest (N=1,008) Baseline Longitudinal
β Estimate (cm3) ± SE P β Estimate (cm3) ± SE P
Total brain volume 4.32 ± 3.93 0.27 −0.95 ± 0.41 0.02
   Frontal lobe volume 0.49 ± 1.89 0.80 −0.33 ± 0.16 0.04
   Temporal lobe volume −0.78 ± 1.06 0.46 −0.16 ± 0.09 0.07
   Parietal lobe volume 1.78 ± 1.07 0.10 −0.15 ± 0.08 0.08
   Occipital lobe volume 0.16 ± 0.85 0.85 −0.08 ± 0.06 0.21
Total white matter 0.62 ± 2.77 0.82 −0.46 ± 0.18 0.01
   Frontal white matter 0.40 ± 1.28 0.75 −0.20 ± 0.08 0.02
   Temporal white matter −0.32 ± 0.66 0.63 −0.09 ± 0.04 0.04
   Parietal white matter 0.70 ± 0.65 0.28 −0.08 ± 0.04 0.08
   Occipital white matter −0.26 ± 0.41 0.53 −0.02 ± 0.02 0.40
Total gray matter 1.00 ± 2.75 0.72 −0.39 ± 0.32 0.22
AD signature region a 0.15 ± 0.40 0.72 −0.06 ± 0.03 0.10
White matter lesions −0.07 ± 0.11 0.53 0.01 ± 0.01 0.70
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Results were derived from linear mixed-effects models adjusting for baseline intracranial volume, age, sex, race, education, APOEε4, comorbidity index (i.e., obesity, hypertension, diabetes, cancer, ischemic heart disease, chronic heart failure, chronic kidney disease and chronic obstructive pulmonary disease), and covariate-time interactions. Betas represent adjusted differences in brain volumes and annual brain volume changes between groups. All results were non-significant after applying FDR correction to adjust for multiple comparisons. Participants without liver disease: n=921, participants with liver disease: n=87 in each brain region of interest, respectively.

Bolded values indicate p<0.05.

a

Combined volume across hippocampus, parahippocampal gyrus, entorhinal cortex, posterior cingulate gyrus, precuneus, cuneus.

Abbreviations: AD, Alzheimer’s Disease.

Figure 2.

Figure 2.

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The predicted MRI brain structure changes among participants with and without liver disease. Predicted results were derived from linear mixed-effects models adjusted for baseline intracranial volume, age, sex, race, education, APOEε4, comorbidity index (i.e., obesity, hypertension, diabetes, cancer, ischemic heart disease, chronic heart failure, chronic kidney disease and chronic obstructive pulmonary disease), and covariate-time interactions. Participants without liver disease: n=921; participants with liver disease: n=87 in each figure. AD signature region is a combined volume of the hippocampus, parahippocampal gyrus, entorhinal cortex, posterior cingulate gyrus, precuneus, and cuneus.

Abbreviations: AD, Alzheimer’s disease.

3.2. Liver Disease and Longitudinal Cognitive Trajectories

We next examined the relationship between liver disease and domain-specific cognitive trajectories in a group of 2,157 participants (baseline age 61 [SD 16]; 51% women; 28% non-White race), 258 (12%) of which had one or more diagnosis of liver disease (participant characteristics in Supplementary Table 5). The mean follow-up time was 9.1 years (median: 7.5; IQR: 2.0, 15.2). The mean number of repeated cognitive assessments was 4.7 (median: 4.0; IQR: 2.0, 7.0). The mean time between liver disease diagnosis and baseline cognitive assessment was 20.6 years (median: 15.0; IQR: 6.0, 31.0).

As displayed in Table 3, liver disease was not associated with significant declines in cognition. Surprisingly, participants with liver disease demonstrated better baseline performance on a composite measure of verbal fluency (β=0.17, SE=0.06; p=0.002). We next examined the relationship between hepatitis diagnosis and cognitive trajectories. Participants with hepatitis did not differ from participants without liver disease on measures of baseline cognition. However, hepatitis diagnosis was associated with accelerated decline on the composite measure of verbal fluency (β=−0.02; SE=0.01; p=0.006; Supplementary Figure 2 and 3; Supplementary Table 6). Analyses that included only participants cognitively normal at baseline (Supplementary Table 7) and adjusted for individual comorbid conditions (rather than a comorbidity index; Supplementary Table 8) found similar results. APOEε4 status and sex did not significantly modify the relationship between liver disease and cognitive decline.

Table 3.

Baseline and longitudinal association of liver disease with cognition

Composite score Baseline Longitudinal
β Estimate ± SE P β Estimate ± SE P
Verbal memory 0.04 ± 0.054 0.43 −0.006 ± 0.005 0.22
Verbal fluency 0.17 ± 0.06 0.002 a −0.006 ± 0.004 0.16
Executive function 0.03 ± 0.05 0.53 −0.002 ± 0.004 0.50
Attention 0.004 ± 0.05 0.94 0.001 ± 0.004 0.77
Visuospatial ability −0.01 ± 0.06 0.87 −0.001 ± 0.005 0.81
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Results were derived from linear mixed-effects models adjusting for baseline age, sex, race, education, APOEε4, comorbidity index (i.e., obesity, hypertension, diabetes, cancer, ischemic heart disease, chronic heart failure, chronic kidney disease and chronic obstructive pulmonary disease), and covariate-time interactions. Betas represent adjusted differences in brain volumes and annual brain volume changes between groups. Sample sizes: verbal memory (liver disease: n=258; non-liver disease: n=1899), verbal fluency (liver disease: n=279; non-liver disease: n=1987), executive function and attention (liver disease: n=291; non-liver disease: n=2167), visuospatial ability (liver disease: n=288; non-liver disease: n=2142). Bolded values indicate p<0.05.

a

Results remained significant after applying FDR correction to adjust for multiple comparisons.

3.3. Liver Disease and Plasma Biomarkers of Alzheimer’s and Neurodegenerative Pathology

Next, we examined the relationship between liver disease and plasma biomarkers of brain amyloid pathology (Aβ42/40), neurodegeneration (NfL), reactive astrogliosis (GFAP), and phosphorylated tau (pTau181). In total, 780 participants with available plasma biomarkers (688 participants for pTau181) were included in this analysis (75 [10%] with a history of liver disease; participant characteristics in Supplementary Table 9). The mean time between diagnosis of liver disease and biomarker measurement was 17.1 years (median: 9.0; IQR: 1.0, 28.0).

In unadjusted analyses, participants with liver disease and participants with hepatitis had lower levels of Aβ42/40 ratio (indicative of greater brain amyloid) and higher NfL (indicative of greater neurodegeneration) compared to individuals without liver disease (Figure 3). In linear regression models that adjusted for demographic factors, kidney function, and chronic medical conditions, only hepatitis maintained significant associations with higher NfL level (β=0.22, SE=0.11, p=0.04), whereas the relationships with Aβ42/40 ratio was no longer significant (Supplementary Table 10). APOEε4 status and sex did not modify the relationship between hepatitis and plasma biomarkers.

Figure 3.

Figure 3.

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Plasma biomarkers of Alzheimer’s disease and neurodegeneration among participants with and without liver disease and hepatitis. Independent samples t-test was used for this analysis. The sample size for Aβ42/Aβ40-ratio, GFAP and NfL: participants without liver disease: n=705; participants with liver disease: n=75; participants with hepatitis: n=32; for pTau181: participants without liver disease: n=632; participants with liver disease: n=56; participants with hepatitis: n=25. The group of participants without hepatitis is the same as participants without liver disease.

Abbreviations: Aβ42/Aβ40-ratio, Amyloid-β42/ Amyloid-β40 ratio; GFAP, Glial Fibrillary Acidic Protein; NfL, Neurofilament Light; pTau181, Phosphorylated tau181.

4. Discussion

The current study examined the association of liver disease with longitudinal trajectories in brain structure and cognition, and employed plasma biomarkers of AD and neurodegeneration to better understand how liver disease may contribute to dementia risk and AD pathogenesis. Liver disease was associated with accelerated total brain volume and regional brain volume loss, particularly in frontal and temporal white matter regions. Importantly, we found no evidence for an association between liver disease and changes in AD signature region volume, white matter lesion volume, or cognitive performance. Additionally, the group of participants with liver disease did not differ from participants without liver disease on plasma biomarkers of brain amyloid, neurodegeneration, or reactive astrogliosis. Hepatitis, a major liver disease subtype, was associated with accelerated declines in verbal fluency and elevated NfL levels. Together, our results indicate that liver disease may be associated with neurodegenerative changes, particularly white matter volume loss. However, these brain changes do not reflect AD-specific pathology or necessarily translate to meaningful cognitive decline.

Previous studies examining brain volume and cognitive functioning among individuals with liver disease have provided mixed results. For instance, several cross-sectional analyses have found that participants with liver disease show smaller total cerebral brain volume, reduced cortical thickness, and decreased subcortical white matter volumes (Chen et al., 2012; Hjerrild et al., 2016; Weinstein et al., 2018). However, other studies have found no such relationships (Basu et al., 2021). While these cross-sectional analyses are informative, they are vulnerable to reverse causation and are limited in their ability to understand how liver disease may affect the trajectory of subsequent brain changes. Nevertheless, longitudinal studies are scarce. One study of approximately 40 participants found an association between HCV and gray and white matter atrophy over 6-7 years (Prell et al., 2019). In a much larger multi-cohort study of individuals with hypertension and diabetes, self-reported liver disease and NAFLD were not associated with longitudinal total brain or abnormal white matter volume (Basu et al., 2021).

Discrepancies between these findings and the results of our study may be accounted for by difference in acquisition of liver disease diagnoses or participant characteristics, or a difference in the liver disease definition, as the current study did not examine a single cause of liver disease (e.g., NAFLD, HCV) in isolation.

There is a clear connection between liver dysfunction and cognition, as illustrated by hepatic encephalopathy which can occur following late-stage liver disease (Chen et al., 2012; Iwasa et al., 2012). However, whether less severe forms of chronic liver disease accelerate cognitive decline remains unknown. Cross-sectional studies suggest that patients with liver disease perform more poorly across multiple cognitive domains (Adekanle et al., 2012; Celikbilek et al., 2018; Karaivazoglou et al., 2007). For example, liver fibrosis, including subclinical liver fibrosis, was independently associated with reduced cognitive function independent of medical comorbidities and lifestyle factors in a recent large cross-sectional analysis (Parikh et al., 2020). However, the few longitudinal studies of cognition do not support an independent relationship between all-cause liver disease or NAFLD and accelerated cognitive decline (Basu et al., 2021; Gerber et al., 2021). The discrepancy between the structural brain changes on MRI related to liver disease and the lack of liver disease related cognitive decline in the current study may be explained by comparatively greater sensitivity of MRI measures, as structural brain changes are more proximal to the biological effects of liver disease and less susceptible than cognition to measurement error. We note, however, that participants with a hepatitis diagnosis did show accelerated decline on measures of verbal fluency. Though this may be a chance finding, it could also suggest that the extent to which liver disease influences cognitive decline depends on the cause of hepatic dysfunction. A link between hepatitis, specifically HCV, and reduced cognition has been supported by numerous cross-sectional studies (Lowry et al., 2010; Monaco et al., 2015; Prell et al., 2019). It is possible that the association of hepatitis, and HCV specifically, with adverse neurocognitive outcomes results from a non-specific neurologic response to systemic viral infection (Butler & Walker, 2021).

Although cohort studies suggest a relationship between liver disease and dementia risk (Chen et al., 2017; Lampignano et al., 2021), whether liver disease increases risk for pathologically defined AD is less clear. A recent study of cognitively normal, MCI, and AD participants demonstrated that the degree of liver function, in addition to being associated with AD diagnosis, was associated with CSF biomarkers that are specific to AD, such as Aβ1-42, and pTau181 (Nho et al., 2019). Another recent study found a relationship between the extent of liver fibrosis and Aβ and tau pathology, as measured with PET imaging, in middle aged adults (Weinstein et al., 2022). We similarly found an association between liver disease and plasma amyloid levels, but these associations did not remain statistically significant after adjusting for potential confounders. Although liver dysfunction may be more common among individuals with AD, our findings suggest that liver disease among cognitively normal individuals does not increase risk for AD-related pathological changes, such as temporal-parietal brain volume loss, verbal memory decline, or abnormal amyloid levels. Notably, studies have demonstrated a relationship between liver disease and markers of cerebral small vessel disease, including white matter lesions and cerebral microbleeds (Kim et al., 2015; Petta et al., 2016). These findings support the idea that liver dysfunction may affect brain health through non-AD cerebrovascular pathways.

The present study has several strengths including the use of a large community sample, longitudinal data, and comprehensive measures of brain health, including neuroimaging, cognitive assessments, and plasma biomarkers. However, the following limitations should be considered when interpreting the present results. First, liver disease diagnosis was based largely on self-report of physician diagnosis, an ascertainment method that is subject to misclassification. Second, detailed measures of liver function and virus activity previously found to be associated with cognitive decline were not examined in the current study. Thus, there was no direct measure of the severity of past liver dysfunction. Third, the present study included several different liver disease diagnoses, and the etiology of hepatitis for some participants was not defined. Distinct types of liver disease likely have different effects on neurobiological processes and resulting neurocognitive trajectories. Accordingly, discrepancies between current findings and results from previous studies may emerge because of our approach to defining liver disease, which included multiple liver disease etiologies and subclinical indicators of liver dysfunction, as defined through ICD-9 classification. Disease-specific effects on brain structure, function, and AD/neurodegeneration biomarkers, which have been shown previously in individuals with NAFLD, HCV, and advanced fibrosis, may have been obscured in the present study given the liver disease heterogeneity. Lastly, the BLSA participants used in the present study are healthier in many respects compared to the general adult population within the United States. For example, the BLSA has much lower rates of NAFLD than the estimated population prevalence (Rich et al., 2018). Thus, the generalizability of the current findings will therefore need to be assessed in population-based studies. Despite these limitations, the present study indicates that in cognitively normal older adults, liver disease – as defined by self-report and ICD-9 classification – may be associated with accelerated neurodegenerative processes in a manner that is distinct from AD pathogenesis.

Supplementary Material

1

Highlights.

  • ICD-9 diagnosis of liver disease is associated with accelerated brain volume loss

  • Atrophy related to liver disease is most prominent in frontal and temporal lobes

  • Hepatitis, but not all-cause liver disease, is associated with cognitive decline

  • Hepatitis, but not all-cause liver disease, is associated with plasma NfL levels

Acknowledgements

The authors thank the BLSA participants and staff for their participation and continued dedication. This research was supported in part by the Intramural Research Program of the NIH, National Institute on Aging. The authors of this manuscript participated in all aspects of the project.

Funding:

This study was supported by the National Institute on Aging (NIA/NIH) Intramural Research Program and in part by grant RF1-AG054409 to C. Davatzikos.

Footnotes

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Credit Author Statement

Zhongsheng Peng: Conceptualization, Methodology, Formal Analysis, Writing – Original Draft, Writing – Review & Editing. Michael R. Duggan: Methodology, Writing – Original Draft, Writing – Review & Editing. Heather E. Dark: Writing – Original Draft, Writing – Review & Editing. Gulzar N. Daya: Writing – Original Draft. Yang An: Methodology, Writing – Review & Editing. Christos Davatzikos: Methodology, Resources, Data Curation, Writing – Review & Editing. Guray Erus: Methodology, Resources, Data Curation, Writing – Review & Editing. Alexandria Lewis: Methodology, Resources, Data Curation. Abhay R. Moghekar: Methodology, Resources, Data Curation, Writing – Review & Editing. Keenan A. Walker: Conceptualization, Methodology, Formal Analysis, Writing – Original Draft, Writing – Review & Editing, Supervision

Disclosure

The authors report no conflicts of interest.

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