The Influence of Metabolic Dysfunction-Associated Steatotic Liver Disease and Body Mass Index on the Incidence of Alzheimer Disease: A Nationwide Cohort Study (original) (raw)

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Tae Seop Lim1,2 , Seok Jong Chung3,4 , Jimin Jeon3,4 , Ja Kyung Kim1,2 , Jinkwon Kim3,4

1Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea; 2Department of Gastroenterology, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University Health System, Yongin, Korea; 3Department of Neurology, Yonsei University College of Medicine, Seoul, Korea; 4Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, Yongin, Korea

Received: February 12, 2025; Revised: May 5, 2025; Accepted: May 19, 2025

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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• Abstract
• Graphical Abstract
• INTRODUCTION
• MATERIALS AND METHODS
• RESULTS
• DISCUSSION
• ACKNOWLEDGEMENTS
• CONFLICTS OF INTEREST
• AUTHOR CONTRIBUTIONS
• DATA AVAILABILITY STATEMENT
• SUPPLEMENTARY MATERIALS
• References

Background/Aims: This study aimed to investigate the influence of metabolic dysfunction-associated steatotic liver disease (MASLD) and body mass index (BMI) on the incidence of Alzheimer disease (AD) in the general South Korean population.
Methods: The National Screening Program for Transitional Ages collected data from 66-year-old dementia-free Koreans in 2010 and 2011. MASLD was diagnosed based on the fatty liver index (≥30) and the presence of metabolic components, and overweight/obese status was defined as a BMI ≥23 kg/m2. The primary outcome was the development of AD up to December 2021. Multivariable Cox analyses were performed to evaluate whether the presence of MASLD or overweight/obese status influenced the risk of developing AD.
Results: A total of 376,902 dementia-free individuals aged 66 years were included in this cohort. The participants were categorized into four groups: overweight/obese non-MASLD (30.4%, n=114,528), overweight/obese MASLD (37.0%, n=139,551), lean non-MASLD (29.9%, n=126,692), and lean MASLD (2.7%, n=10,131). During a mean follow-up period of 10.38±1.90 years, 23,874 individuals (6.3%) were newly diagnosed with AD. Compared to the overweight/obese non-MASLD group, the adjusted hazard ratios (95% confidence interval) for AD in the lean MASLD, lean non-MASLD, and overweight/obese MASLD groups were 1.34 (1.24 to 1.45), 1.08 (1.04 to 1.13), and 1.13 (1.09 to 1.17), respectively.
Conclusions: A normal/underweight BMI and the presence of MASLD synergistically increased the risk of AD. The lean MASLD group had a higher risk of developing AD than the overweight/obese MASLD group, suggesting that the clinical relevance of MASLD for incident AD differs based on the BMI.

Keywords: Alzheimer disease, Dementia, Metabolic dysfunction-associated steatotic liver disease, Fatty liver

INTRODUCTION

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• Abstract
• Graphical Abstract
• INTRODUCTION
• MATERIALS AND METHODS
• RESULTS
• DISCUSSION
• ACKNOWLEDGEMENTS
• CONFLICTS OF INTEREST
• AUTHOR CONTRIBUTIONS
• DATA AVAILABILITY STATEMENT
• SUPPLEMENTARY MATERIALS
• References

Alzheimer disease (AD) is the most common neurodegenerative disorder, characterized by progressive cognitive decline and memory loss. With the global population aging, the prevalence of AD is rapidly increasing, posing a significant public health challenge.1 Therefore, understanding the risk factors for AD is crucial to develop effective prevention and intervention strategies.

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as nonalcoholic fatty liver disease, is the most common chronic liver disease, affecting approximately one in three individuals worldwide.2 The rapid increase in the prevalence of MASLD is expected to persist in the forthcoming years, propelled by escalating obesity rates, an aging demographic, unhealthy diets, and the widespread adoption of sedentary lifestyles.3,4 Several lines of evidence suggest that unfavorable clinical outcomes in individuals with MASLD result not only from the liver pathology itself, but also from the extrahepatic complications related to metabolic abnormalities.5,6 There is a high burden of metabolic comorbidities associated with MASLD, including diabetes mellitus, dyslipidemia, and hypertension as well as higher body mass index (BMI).7

Some studies have shown that MASLD is associated with an increased risk of dementia,8-11 while others have reported no association between MASLD and incident dementia.12-15 These inconsistent findings may be due to differences in study design or types of dementia in each study.16 Alternatively, complex relationships between the major components of MASLD (i.e., liver dysfunction,17 metabolic abnormalities,18 and obesity19 and incident dementia might contribute to heterogeneous results. For example, metabolic dysfunction is known to increase the risk of developing dementia,18 whereas increased BMI after the age of 65 years may be a marker of decreased dementia risk,19 suggesting varying clinical implications of MASLD subtypes based on BMI. In the present study, we aimed to investigate the combined effect of MASLD and BMI on the incidence of AD in the general population of South Korea using a nationwide population-based dataset.

1. Study design and participants

This retrospective cohort study used data from the National Screening Program for Transitional Ages (NSPTA) in South Korea. To promote public health, South Korea provides the nationwide life transition period health examination to all 66-year-old Koreans free of charge.20 This examination consists of a survey regarding medical history and lifestyle, physical examination, and blood chemistry. Using data from the NSPTA between 2010 and 2011, we recruited a cohort of 66-year-old participants without dementia. Exclusion criteria were as follows: (1) presence of the related diagnostic code of any dementia (F00, F01, F03, G30, G31) before the NSPTA examination; (2) prior history of diagnostic code of other liver disease including viral hepatitis (B15–B19), alcoholic liver disease (K70), autoimmune liver disease (K83.0, K74.3, K75.4), hemochromatosis (E83.1), Wilson disease (E83.0), Budd-Chiari syndrome (I82.0, K76.5); (3) excessive alcohol intake (males: ≥210 g/ethanol/week; females: ≥140 g/ethanol/week) based on the survey in NSPTA; (4) follow-up of less than 1 year or diagnosis of AD within 1 year since NSPTA examination; and (5) incomplete data used in the analysis. The index date was defined as the date of the life transition period of the health examination at the age of 66 years.

The National Health Insurance System (NHIS) in South Korea is a public single-payer system for healthcare services. The participation of all Koreans and healthcare providers in the single-payer insurance system is mandatory. For the payment and review of health claims, data on healthcare utilization were submitted to the NHIS. The health claims database contains information on hospital visits, medical diagnoses (International Statistical Classification of Diseases and Related Health Problems [ICD]-10 codes), procedures, and individual prescription records. For political and academic research, the anonymized health claims database has been opened to researchers and is a great resource for medical research. Through a review of the claims database, we identified new diagnoses of AD in the study participants. This study was approved by the Institutional Review Board of Yongin Severance Hospital, Yongin, Korea (IRB number: 9-2023-0265), and the requirement for informed consent was waived because of the retrospective analysis and use of a fully anonymized dataset.

2. Diagnosis of AD and follow-up

The primary outcome was newly diagnosed AD after the index date. New-onset AD was identified when participants had at least two health claims with the primary diagnosis for AD (ICD-10 codes of F00 or G30) accompanied by the prescription of cholinesterase inhibitors (donepezil, rivastigmine, and galantamine) or memantine for the treatment of AD.21 Study participants were followed up until the development of AD, loss to follow-up, death, or December 2021, whichever occurred first.

3. Covariates

We collected information on sex, household income level (quartiles) in the year of the NSPTA, presence of comorbidities (hypertension, diabetes mellitus, dyslipidemia, and chronic kidney disease), results of physical examination, and laboratory tests performed in the NSPTA. Definitions of comorbidities are available in Supplementary Table 1. Data collected during the physical examination in the NSPTA included BMI, waist circumference, systolic/diastolic blood pressure, and hearing loss. Pure-tone audiometric tests were conducted in both ears. Hearing loss was defined at pure-tone average thresholds of 0.5, 1.0, and 2.0 kHz >40 dB in one or both ears.22 Available laboratory data obtained from fasting serum were levels of fasting glucose, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol (HDL-C), triglycerides, aspartate aminotransferase, alanine aminotransferase, gamma-glutamyl transferase, and estimated glomerular filtration rate based on serum creatinine. NSPTA contained short questionnaires for screening of subjective cognitive decline (Prescreening Korean Dementia Screening Questionnaires: KDSQ-P score).23 KDSQ-P score ranges from 0 to 10 and a higher KDSQ-P score indicates more severe cognitive decline. Participants were categorized into KDSQ-P scores ≥4 and <4. Information about smoking habits (never, ex-smoker, current smoker), alcohol consumption, and physical activity was also evaluated based on self-administered lifestyle questionnaires.24

4. Hepatic steatosis, MASLD, and overweight/obese status

Hepatic steatosis was evaluated using fatty liver index (FLI) (Supplementary Table 2), which is recognized by the guidelines as an alternative to imaging techniques for large epidemiological studies.25,26 FLI was validated in the Korean population, demonstrating an area under the receiver operating characteristic curve of 0.87.27 A cutoff value of FLI ≥30 was utilized, consistent with previous Korean studies.28,29

MASLD was defined as the presence of hepatic steatosis (FLI ≥30) with one or more of the following criteria of metabolic abnormality: (1) BMI ≥23 kg/m2 or (waist circumference ≥90 cm in men or ≥80 cm in women); (2) fasting glucose ≥100 mg/dL or diagnosis of diabetes mellitus or treatment with anti-diabetic medications; (3) systolic blood pressure ≥130 mm Hg or diastolic blood pressure ≥85 mm Hg or treatment with antihypertensive medication; (4) triglyceride ≥150 mg/dL or treatment with lipid-lowering medication; (5) (HDL-C ≤40 mg/dL in male or HDL-C ≤50 mg/dL in female) or treatment with lipid-lowering medication.30

According to the presence of MASLD and BMI (overweight/obese: BMI ≥23 kg/m2, lean: BMI <23 kg/m2), study participants were classified into four groups: overweight/obese non-MASLD, overweight/obese MASLD, lean non-MASLD, and lean MASLD.

5. Statistical analysis

Clinical characteristics were represented as numbers (%) for categorical variables and mean±standard deviation for continuous variables. Differences between groups were compared using the chi-square test or independent t-test. Cumulative incidence curves for AD were illustrated according to the presence of MASLD and overweight/obese status, and differences in the curves were compared using a log-rank test. To identify risk factors for AD, we constructed a Cox proportional hazards regression model. The proportional hazards assumption of the Cox regression model was evaluated by calculating Schoenfeld residuals, which were satisfied. To evaluate the relationship between MASLD, BMI, and the risk of AD, adjustments were made for sex, household income, smoking habits, alcohol consumption, physical activity, chronic kidney disease, systolic blood pressure, fasting serum glucose, low-density lipoprotein cholesterol, KDSQ-P, hearing loss, and BMI. We additionally performed subgroup analyses in individuals with MASLD to investigate the impact of advanced liver fibrosis, which was defined by BARD score ≥2,31 on the risk of AD (Supplementary Table 2). We also performed sensitivity analyses to evaluate the relationship between hepatic steatosis (i.e., FLI ≥30 or 60) regardless of the metabolic abnormality, BMI, and the risk of AD. Additionally, we also performed sensitivity analyses of latent period exclusion, by excluding those who died, were diagnosed with AD within the first 2 or 4 years since the index date, or had a follow-up duration of less than 2 or 4 years.

Statistical analyses were performed using the R software, version 3.3.3 (R Foundation for Statistical Computing, Vienna, Austria) and SAS 9.4 version (SAS Inc., Cary, NC, USA). Two-sided p-values less than 0.05 were considered significant.

6. Data availability

The data used in this study were obtained from the NHIS database of South Korea. Access to these data is restricted and was granted under a specific license for the purposes of this study (NHIS-2023-1-373); therefore, the data are not publicly accessible. Data are available upon reasonable request, with review and approval from the National Health Insurance Service Research Committee (https://nhiss.nhis.or.kr/bd/ay/bdaya001iv.do).

RESULTS

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• Abstract
• Graphical Abstract
• INTRODUCTION
• MATERIALS AND METHODS
• RESULTS
• DISCUSSION
• ACKNOWLEDGEMENTS
• CONFLICTS OF INTEREST
• AUTHOR CONTRIBUTIONS
• DATA AVAILABILITY STATEMENT
• SUPPLEMENTARY MATERIALS
• References

1. Demographic and clinical characteristics

From 2010 to 2011, a total of 460,481 Koreans aged 66 years underwent the NSPTA. Following the study criteria and excluding participants with significant alcohol use, prior liver disease, dementia history, insufficient follow-up period or missing data, 376,902 participants were included in the analysis (Fig. 1). Baseline characteristics of the study participants are presented in Table 1. Males accounted for 41.5% of the study population and MASLD was present in 39.7%. Regarding the presence of MASLD and overweight/obese status (BMI ≥23 kg/m2), the proportions of overweight/obese non-MASLD, overweight/obese MASLD, lean non-MASLD, and lean MASLD were 30.4%, 37.0%, 29.9%, and 2.7%, respectively. Differences in characteristics according to the four categories are shown in Supplement Table 3.

Figure 1.Flowchart of study participant’s inclusion and exclusion.

Table 1

Characteristics of Study Participants

Data are presented as number (%), mean±SD, or median (IQR).

Q, quartile; MET, metabolic equivalent task; BP, blood pressure; eGFR, estimated glomerular filtration rate; KDSQ-P, Prescreening Korean Dementia Screening Questionnaire; FLI, fatty liver index; MASLD, metabolic dysfunction-associated steatotic liver diseasemean.

2. MASLD, overweight/obese, and the risk of AD

During the mean follow-up of 10.38±1.90 years, there were 23,874 individuals who received a new diagnosis of AD (6.33%). First, we analyzed the individual associations between MASLD, overweight/obese status, and the risk of AD. Compared with lean participants (BMI <23 kg/m2), overweight/obese participants (BMI ≥23 kg/m2) had a 0.92-fold lower risk of AD (adjusted hazard ratio [HR], 0.92; 95% confidence interval [CI], 0.88 to 0.96; p<0.001) (Supplementary Table 4, Model 1). When BMI was treated as a continuous variable, higher BMI was associated with a lower risk of AD (adjusted HR, 0.98; 95% CI, 0.97 to 0.98; p<0.001) (Table 2). Participants with MASLD had a higher risk for developing AD than those with non-MASLD (adjusted HR, 1.15; 95% CI, 1.11–1.18; p<0.001) (Supplementary Table 4, Model 2).

Table 2

Risk Factors for the Development of Alzheimer Disease

Data regarding the development of Alzheimer disease were derived from the Cox proportional hazards regression model.

HR, hazard ratio; CI, confidence interval; Q, quartile; MET, metabolic equivalent task; LDL, low-density lipoprotein; KDSQ-P, Prescreening Korean Dementia Screening Questionnaire; BMI, body mass index; MASLD, metabolic dysfunction-associated steatotic liver disease.

*Adjusted for the variables listed in this table (sex, household income, smoking status, alcohol consumption, physical activity, chronic kidney disease, systolic blood pressure, fasting glucose, LDL cholesterol, KDSQ-P ≥4, hearing loss, BMI, and MASLD category.

We further assessed the risk of incident AD based on the presence of MASLD and overweight/obese status. Compared with individuals with overweight/obese non-MASLD (570.69 cases per 100,000 person-years; reference), the incidence rate of AD was higher in those with overweight/obese MASLD (601.99 cases per 100,000 person-years), lean non-MASLD (651.27 cases per 100,000 person-years), and lean MASLD (736.08 cases per 100,000 person-years) (Supplementary Table 4, Model 3). Cumulative incidence curves for the development of AD (Fig. 2) showed that the risk of AD differed across the groups (log-rank p<0.001). Multivariate Cox proportional regression model for the development of AD demonstrated that with the reference of overweight/obese non-MASLD, the adjusted HRs (95% CI) for lean MASLD, overweight/obese MASLD, and lean non-MASLD were 1.34 (1.24 to 1.45), 1.13 (1.09 to 1.17), and 1.08 (1.04 to 1.13), respectively (p<0.001).

Figure 2.Cumulative incidence curve for the development of Alzheimer disease. MASLD, metabolic dysfunction-associated steatotic liver disease.

The results of subgroup analyses according to the presence of advanced liver fibrosis (BARD score ≥2) in individuals with MASLD are shown in Table 3. In overweight/obese MASLD subjects, advanced liver fibrosis was associated with an increased risk of AD (adjusted HR, 1.08; 95% CI, 1.01 to 1.14; p=0.014). In lean MASLD subjects, the presence of advanced liver fibrosis also tended to be associated with an increased risk of incident AD (adjusted HR, 1.21; 95% CI, 0.99 to 1.48; p=0.070).

Table 3

Incidence of Alzheimer Disease Based on the Presence of Advanced Liver Fibrosis in MASLD

MASLD, metabolic dysfunction-associated steatotic liver disease; HR, hazard ratio; CI, confidence interval.

*Adjusted for sex, household income, smoking status, alcohol consumption, physical activity, chronic kidney disease, systolic blood pressure, fasting glucose, low-density lipoprotein cholesterol, Prescreening Korean Dementia Screening Questionnaire (≥4), hearing loss, and body mass index.

The results of sensitivity analyses according to the presence of hepatic steatosis (FLI ≥30 or 60) regardless of the presence of metabolic abnormality are shown in Supplementary Table 4. When the presence of hepatic steatosis was defined as FLI ≥30, the incidence of AD was comparable between individuals without (610.32 cases per 100,000 person-years) and with hepatic steatosis (610.62 cases per 100,000 person-years). Multivariable Cox proportional regression model demonstrated that the presence of hepatic steatosis increased the risk for the development of AD (adjusted HR, 1.15; 95% CI, 1.11 to 1.18; p<0.001) (Supplementary Table 4, Model 4). When we divided the study participants into four groups according to the presence of hepatic steatosis (FLI ≥30) and overweight/obese (BMI ≥23 kg/m2) status, the risk of AD differed across the groups. Compared with individuals with overweight/obese non-hepatic steatosis (570.69 cases per 100,000 person-years; reference), the incidence rate of AD was higher in overweight/obese individuals with hepatic steatosis (601.99 cases per 100,000 person-years: adjusted HR, 1.13; 95% CI, 1.09 to 1.18, p<0.001), lean individuals without hepatic steatosis (651.53 cases per 100,000 person-years: adjusted HR, 1.08; 95% CI, 1.04 to 1.13; p<0.001), and lean individuals with hepatic steatosis (730.46 cases per 100,000 person-years: adjusted HR, 1.34; 95% CI, 1.23 to 1.44; p<0.001) (Supplementary Table 4, Model 5). When the presence of hepatic steatosis was defined as FLI ≥60, we also obtained similar results (Supplementary Table 4, Models 6 and 7).

The results of sensitivity analyses of latent period exclusion are shown in Supplementary Table 5. When we excluded the study participants who were lost to follow-up within 2 years (or 4 years) or diagnosis of AD within 2 years (or 4 years) since NSPTA examination, we still obtained consistent results that the order of increasing AD risk was lean MASLD (highest), overweight/obese MASLD, lean non-MASLD, and overweight/obese non-MASLD (lowest).

DISCUSSION

Go to

• Abstract
• Graphical Abstract
• INTRODUCTION
• MATERIALS AND METHODS
• RESULTS
• DISCUSSION
• ACKNOWLEDGEMENTS
• CONFLICTS OF INTEREST
• AUTHOR CONTRIBUTIONS
• DATA AVAILABILITY STATEMENT
• SUPPLEMENTARY MATERIALS
• References

In the present study, we investigated the influence of both MASLD and BMI on the incidence of AD in the general population of South Korea. We constructed a retrospective cohort of 66-year-old dementia-free Korean participants using data from the national screening program, and reviewed the claims data from the NHIS to identify a new diagnosis of AD in these study participants. Our major findings were as follows: (1) during a mean follow-up of 10.38 years, 23,874 individuals (6.33%) aged 66 were newly diagnosed with AD; (2) participants with MASLD at baseline had a higher risk of developing AD than those without MASLD. Overweight/obese participants (BMI ≥23 kg/m2) had a 0.92-fold lower risk of AD than lean participants (BMI <23 kg/m2); (3) when we further assessed the risk of incident AD based on the presence of MASLD and overweight/obese status, the order of increasing AD risk was lean MASLD (highest), overweight/obese MASLD, lean non-MASLD, and overweight/obese non-MASLD (lowest); and (4) advanced liver fibrosis was associated with an increased risk of AD in overweight/obese individuals with MASLD. Taken together, our findings suggest that the impact of MASLD on AD risk becomes evident when individuals are stratified by BMI and that advanced liver fibrosis in MASLD is associated with an increased risk of developing AD.

Accumulating evidence indicates that MASLD and dementia share common biological mechanisms (i.e., insulin resistance, systemic inflammation, and prothrombotic state32, and can potentially affect each other, consistent with the emerging concept of liver-brain axis.33 Disruption of the gut microbiota,34 hyperammonemia,35 and smaller total cerebral brain volumes,36 which are often observed in individuals with MASLD, may also accelerate cognitive decline. Moreover, evidence suggests that the liver is involved in brain β-amyloid deposits as well as peripheral clearance of circulating β-amyloid in the blood,37 which may contribute to the pathogenesis of dementia. Despite this evidence, previous epidemiological studies investigating the association between MASLD and incident dementia have yielded inconsistent results, with some reporting that MASLD increases the risk of developing dementia,8-11 while others reported that MASLD is not associated with incident dementia12-15 or even lowers the risk of dementia.12 The discrepant findings across studies are partly explained by differences in the study populations, study designs, and operational definitions of MASLD and dementia. Another possible explanation is that the relationship between MASLD and the risk of dementia may vary depending on the subtype of MASLD and may require analyses by subtype. For example, although obesity and metabolic disorders are often interrelated in MASLD, they can also exist independently in some individuals (i.e., the lean MASLD subtype) with different clinical implications. This may be in line with the recently introduced concept of “metabolically healthy obesity” and “metabolically obese, but normal weight” to explain the heterogeneous nature of obesity.38

In the present study, individuals with MASLD had a higher risk of developing AD than those without MASLD in the overall cohort. When the study participants were further stratified based on their BMI status, we found that the presence of MASLD was associated with incident AD in the following order: lean MASLD (highest) > overweight/obese MASLD > lean non-MASLD > overweight/obese non-MASLD (lowest). It is well established that all components of metabolic abnormalities of MASLD (i.e., obesity, insulin resistance, hypertension, and dyslipidemia) increase the risk of developing dementia.18 Metabolic disorders in MASLD can lead to a variety of microvascular lesions, which subsequently accelerate the occurrence of cognitive decline and dementia.39 However, regarding the relationship between obesity and dementia, opposite results (i.e., obesity reduces the risk of dementia) have also been reported in many studies, consistent with the concept of the obesity paradox.19 Evidence suggests that BMI is determined by several factors that are closely linked to neurodegenerative processes in the preclinical stages of AD, including disturbed energy homeostasis, hypothalamic dysregulation and sarcopenia, as well as gastrointestinal dysfunction, impaired olfaction, and reduced motivation.40 Both high and low BMI can involve a series of pathological cellular responses leading to neurodegeneration in AD, via adiposity-related inflammation41 and low central insulin levels,42 respectively. One possible explanation for these mixed results across studies is that the association between BMI and AD risk is likely attributable to two separate processes in the development of AD:19,43 higher BMI in middle-aged individuals may be linked to an increased risk of AD, whereas lower BMI associated with preclinical metabolic changes in late life may be observed before AD diagnosis. In this study, we found that late-life overweight or obesity was associated with a lower risk of developing AD.44 In particular, our finding that the impact of MASLD on AD risk became more evident after stratification by BMI status suggests that being overweight or obese may be more strongly associated with the risk of developing AD than MASLD. Taken together, active screening for AD in underweight populations in later life could be a more efficient public health strategy for reducing the burden of AD. Early intervention for the metabolic comorbidities associated with MASLD may also be an effective therapeutic strategy for delaying the onset of AD.

We performed subgroup and sensitivity analyses to assess the relationship between liver integrity and the risk of AD. Hepatic steatosis, regardless of the presence of metabolic abnormalities, was associated with incident AD, and the presence of advanced liver fibrosis further strengthened the risk of AD in individuals with MASLD. It is well recognized that individuals with chronic liver disease or liver cirrhosis are more vulnerable to frailty compared to those without chronic liver disease,45 which may predispose to future cognitive decline and dementia.15 Experimental studies have shown that liver fibrosis is characterized by a proinflammatory state46 and shares a metabolic milieu of insulin resistance, adipokine secretion, and oxidative stress,47 facilitating the detrimental effects of cardiometabolic risk factors on the brain. Furthermore, some evidence suggests that advanced liver fibrosis is associated with AD-related pathological burden in the brain, independently of cardiometabolic risk factors.48 Our findings indicate that the liver itself plays a considerable role in the risk of incident dementia and timely intervention in liver dysfunction can help delay brain aging.

Our study has some limitations. First, hepatic steatosis and advanced liver fibrosis were defined using non-invasive scores without imaging or pathologic confirmation. However, these noninvasive methods have undergone extensive validation,27,49 and guidelines recognize the usefulness of noninvasive surrogates in large epidemiological studies.25,26 There may also be inaccuracies in the diagnosis of AD based on the national health claims data, as it was not originally created for research purposes. Second, it may be worth considering whether the relationship between MASLD and subsequent AD risk varies depending on the exposure duration or severity of MASLD, although this information was not available in the cross-sectional dataset from the NSPTA. Third, it may be oversimplifying to define overweight/obese status by categorizing the BMI based on a cutoff of 23 kg/m2. However, when we performed additional analyses by dividing the BMI categories as underweight/normal weight (BMI <23 kg/m2), overweight (23 kg/m2≤BMI<25 kg/m2), and obesity (BMI ≥25 kg/m2),50 we found that additional categorization of BMI using two cutoff values (i.e., 23 and 25 kg/m2) did not provide additional information on the risk for incident AD (Supplementary Table 4, Model 8). Furthermore, BMI may not be a good measure of adiposity in the elderly and may instead be an indicator of frailty.19 Muscle mass is also reflected in BMI, which can be protective against neurodegenerative processes.51,52 It may be cautiously hypothesized that lower BMI observed in lean MASLD subjects could reflect decreased muscle mass or sarcopenia, potentially increasing the AD risk identified in this group. Fourth, other potential risk factors for AD, such as apolipoprotein E genotyping and educational attainment, were not available for this nationwide cohort.

In conclusion, our findings suggest that the combination of normal/underweight BMI and MASLD synergistically increases the risk of developing AD. Lean MASLD was associated with a higher risk of AD than overweight/obese MASLD, indicating that the clinical significance of MASLD in relation to AD onset varies among the different MASLD subtypes. The interplay between MASLD, BMI, and AD risk is complex and warrants further prospective studies to evaluate the complex interactions among body composition, metabolic health, and neurodegenerative disorders, which might inform more effective interventions and preventive strategies in public health. In particular, our findings suggest the potential benefit of personalized clinical strategies for managing lean MASLD patients, emphasizing muscle preservation as well as weight management to potentially mitigate AD risk.

This study was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (grant number: RS-2024-00345524). This study was also supported by the Research Supporting Program of the Korean Association for the Study of the Liver and The Korean Liver Foundation (2024-31-0934).

MID (Medical Illustration & Design), as a member of the Medical Research Support Services of Yonsei University College of Medicine, providing excellent support with medical illustration.

J.K.K. is an editorial board member of the journal but was not involved in the peer reviewer selection, evaluation, or decision process of this article. No other potential conflicts of interest relevant to this article were reported.

Study concept and design: T.S.L., S.J.C. Data acquisition: J.J., J.K. Data analysis and interpretation: J.J., J.K. Drafting of the manuscript: T.S.L., S.J.C. Critical revision of the manuscript for important intellectual content: J.K., J.K.K. Statistical analysis: J.J., J.K. Obtained funding: T.S.L., J.K. Administrative, technical, or material support: S.J.C. Study supervision: J.K., J.K.K. Approval of final manuscript: All authors.

Data analyzed in this study are available from the corresponding author upon reasonable request.

References

Go to

• Abstract
• Graphical Abstract
• INTRODUCTION
• MATERIALS AND METHODS
• RESULTS
• DISCUSSION
• ACKNOWLEDGEMENTS
• CONFLICTS OF INTEREST
• AUTHOR CONTRIBUTIONS
• DATA AVAILABILITY STATEMENT
• SUPPLEMENTARY MATERIALS
• References

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Article

Original Article

The Influence of Metabolic Dysfunction-Associated Steatotic Liver Disease and Body Mass Index on the Incidence of Alzheimer Disease: A Nationwide Cohort Study

Tae Seop Lim1,2 , Seok Jong Chung3,4 , Jimin Jeon3,4 , Ja Kyung Kim1,2 , Jinkwon Kim3,4

1Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea; 2Department of Gastroenterology, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University Health System, Yongin, Korea; 3Department of Neurology, Yonsei University College of Medicine, Seoul, Korea; 4Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, Yongin, Korea

Received: February 12, 2025; Revised: May 5, 2025; Accepted: May 19, 2025

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background/Aims: This study aimed to investigate the influence of metabolic dysfunction-associated steatotic liver disease (MASLD) and body mass index (BMI) on the incidence of Alzheimer disease (AD) in the general South Korean population.
Methods: The National Screening Program for Transitional Ages collected data from 66-year-old dementia-free Koreans in 2010 and 2011. MASLD was diagnosed based on the fatty liver index (≥30) and the presence of metabolic components, and overweight/obese status was defined as a BMI ≥23 kg/m2. The primary outcome was the development of AD up to December 2021. Multivariable Cox analyses were performed to evaluate whether the presence of MASLD or overweight/obese status influenced the risk of developing AD.
Results: A total of 376,902 dementia-free individuals aged 66 years were included in this cohort. The participants were categorized into four groups: overweight/obese non-MASLD (30.4%, n=114,528), overweight/obese MASLD (37.0%, n=139,551), lean non-MASLD (29.9%, n=126,692), and lean MASLD (2.7%, n=10,131). During a mean follow-up period of 10.38±1.90 years, 23,874 individuals (6.3%) were newly diagnosed with AD. Compared to the overweight/obese non-MASLD group, the adjusted hazard ratios (95% confidence interval) for AD in the lean MASLD, lean non-MASLD, and overweight/obese MASLD groups were 1.34 (1.24 to 1.45), 1.08 (1.04 to 1.13), and 1.13 (1.09 to 1.17), respectively.
Conclusions: A normal/underweight BMI and the presence of MASLD synergistically increased the risk of AD. The lean MASLD group had a higher risk of developing AD than the overweight/obese MASLD group, suggesting that the clinical relevance of MASLD for incident AD differs based on the BMI.

Keywords: Alzheimer disease, Dementia, Metabolic dysfunction-associated steatotic liver disease, Fatty liver

INTRODUCTION

Alzheimer disease (AD) is the most common neurodegenerative disorder, characterized by progressive cognitive decline and memory loss. With the global population aging, the prevalence of AD is rapidly increasing, posing a significant public health challenge.1 Therefore, understanding the risk factors for AD is crucial to develop effective prevention and intervention strategies.

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as nonalcoholic fatty liver disease, is the most common chronic liver disease, affecting approximately one in three individuals worldwide.2 The rapid increase in the prevalence of MASLD is expected to persist in the forthcoming years, propelled by escalating obesity rates, an aging demographic, unhealthy diets, and the widespread adoption of sedentary lifestyles.3,4 Several lines of evidence suggest that unfavorable clinical outcomes in individuals with MASLD result not only from the liver pathology itself, but also from the extrahepatic complications related to metabolic abnormalities.5,6 There is a high burden of metabolic comorbidities associated with MASLD, including diabetes mellitus, dyslipidemia, and hypertension as well as higher body mass index (BMI).7

Some studies have shown that MASLD is associated with an increased risk of dementia,8-11 while others have reported no association between MASLD and incident dementia.12-15 These inconsistent findings may be due to differences in study design or types of dementia in each study.16 Alternatively, complex relationships between the major components of MASLD (i.e., liver dysfunction,17 metabolic abnormalities,18 and obesity19 and incident dementia might contribute to heterogeneous results. For example, metabolic dysfunction is known to increase the risk of developing dementia,18 whereas increased BMI after the age of 65 years may be a marker of decreased dementia risk,19 suggesting varying clinical implications of MASLD subtypes based on BMI. In the present study, we aimed to investigate the combined effect of MASLD and BMI on the incidence of AD in the general population of South Korea using a nationwide population-based dataset.

MATERIALS AND METHODS

1. Study design and participants

This retrospective cohort study used data from the National Screening Program for Transitional Ages (NSPTA) in South Korea. To promote public health, South Korea provides the nationwide life transition period health examination to all 66-year-old Koreans free of charge.20 This examination consists of a survey regarding medical history and lifestyle, physical examination, and blood chemistry. Using data from the NSPTA between 2010 and 2011, we recruited a cohort of 66-year-old participants without dementia. Exclusion criteria were as follows: (1) presence of the related diagnostic code of any dementia (F00, F01, F03, G30, G31) before the NSPTA examination; (2) prior history of diagnostic code of other liver disease including viral hepatitis (B15–B19), alcoholic liver disease (K70), autoimmune liver disease (K83.0, K74.3, K75.4), hemochromatosis (E83.1), Wilson disease (E83.0), Budd-Chiari syndrome (I82.0, K76.5); (3) excessive alcohol intake (males: ≥210 g/ethanol/week; females: ≥140 g/ethanol/week) based on the survey in NSPTA; (4) follow-up of less than 1 year or diagnosis of AD within 1 year since NSPTA examination; and (5) incomplete data used in the analysis. The index date was defined as the date of the life transition period of the health examination at the age of 66 years.

The National Health Insurance System (NHIS) in South Korea is a public single-payer system for healthcare services. The participation of all Koreans and healthcare providers in the single-payer insurance system is mandatory. For the payment and review of health claims, data on healthcare utilization were submitted to the NHIS. The health claims database contains information on hospital visits, medical diagnoses (International Statistical Classification of Diseases and Related Health Problems [ICD]-10 codes), procedures, and individual prescription records. For political and academic research, the anonymized health claims database has been opened to researchers and is a great resource for medical research. Through a review of the claims database, we identified new diagnoses of AD in the study participants. This study was approved by the Institutional Review Board of Yongin Severance Hospital, Yongin, Korea (IRB number: 9-2023-0265), and the requirement for informed consent was waived because of the retrospective analysis and use of a fully anonymized dataset.

2. Diagnosis of AD and follow-up

The primary outcome was newly diagnosed AD after the index date. New-onset AD was identified when participants had at least two health claims with the primary diagnosis for AD (ICD-10 codes of F00 or G30) accompanied by the prescription of cholinesterase inhibitors (donepezil, rivastigmine, and galantamine) or memantine for the treatment of AD.21 Study participants were followed up until the development of AD, loss to follow-up, death, or December 2021, whichever occurred first.

3. Covariates

We collected information on sex, household income level (quartiles) in the year of the NSPTA, presence of comorbidities (hypertension, diabetes mellitus, dyslipidemia, and chronic kidney disease), results of physical examination, and laboratory tests performed in the NSPTA. Definitions of comorbidities are available in Supplementary Table 1. Data collected during the physical examination in the NSPTA included BMI, waist circumference, systolic/diastolic blood pressure, and hearing loss. Pure-tone audiometric tests were conducted in both ears. Hearing loss was defined at pure-tone average thresholds of 0.5, 1.0, and 2.0 kHz >40 dB in one or both ears.22 Available laboratory data obtained from fasting serum were levels of fasting glucose, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol (HDL-C), triglycerides, aspartate aminotransferase, alanine aminotransferase, gamma-glutamyl transferase, and estimated glomerular filtration rate based on serum creatinine. NSPTA contained short questionnaires for screening of subjective cognitive decline (Prescreening Korean Dementia Screening Questionnaires: KDSQ-P score).23 KDSQ-P score ranges from 0 to 10 and a higher KDSQ-P score indicates more severe cognitive decline. Participants were categorized into KDSQ-P scores ≥4 and <4. Information about smoking habits (never, ex-smoker, current smoker), alcohol consumption, and physical activity was also evaluated based on self-administered lifestyle questionnaires.24

4. Hepatic steatosis, MASLD, and overweight/obese status

Hepatic steatosis was evaluated using fatty liver index (FLI) (Supplementary Table 2), which is recognized by the guidelines as an alternative to imaging techniques for large epidemiological studies.25,26 FLI was validated in the Korean population, demonstrating an area under the receiver operating characteristic curve of 0.87.27 A cutoff value of FLI ≥30 was utilized, consistent with previous Korean studies.28,29

MASLD was defined as the presence of hepatic steatosis (FLI ≥30) with one or more of the following criteria of metabolic abnormality: (1) BMI ≥23 kg/m2 or (waist circumference ≥90 cm in men or ≥80 cm in women); (2) fasting glucose ≥100 mg/dL or diagnosis of diabetes mellitus or treatment with anti-diabetic medications; (3) systolic blood pressure ≥130 mm Hg or diastolic blood pressure ≥85 mm Hg or treatment with antihypertensive medication; (4) triglyceride ≥150 mg/dL or treatment with lipid-lowering medication; (5) (HDL-C ≤40 mg/dL in male or HDL-C ≤50 mg/dL in female) or treatment with lipid-lowering medication.30

According to the presence of MASLD and BMI (overweight/obese: BMI ≥23 kg/m2, lean: BMI <23 kg/m2), study participants were classified into four groups: overweight/obese non-MASLD, overweight/obese MASLD, lean non-MASLD, and lean MASLD.

5. Statistical analysis

Clinical characteristics were represented as numbers (%) for categorical variables and mean±standard deviation for continuous variables. Differences between groups were compared using the chi-square test or independent t-test. Cumulative incidence curves for AD were illustrated according to the presence of MASLD and overweight/obese status, and differences in the curves were compared using a log-rank test. To identify risk factors for AD, we constructed a Cox proportional hazards regression model. The proportional hazards assumption of the Cox regression model was evaluated by calculating Schoenfeld residuals, which were satisfied. To evaluate the relationship between MASLD, BMI, and the risk of AD, adjustments were made for sex, household income, smoking habits, alcohol consumption, physical activity, chronic kidney disease, systolic blood pressure, fasting serum glucose, low-density lipoprotein cholesterol, KDSQ-P, hearing loss, and BMI. We additionally performed subgroup analyses in individuals with MASLD to investigate the impact of advanced liver fibrosis, which was defined by BARD score ≥2,31 on the risk of AD (Supplementary Table 2). We also performed sensitivity analyses to evaluate the relationship between hepatic steatosis (i.e., FLI ≥30 or 60) regardless of the metabolic abnormality, BMI, and the risk of AD. Additionally, we also performed sensitivity analyses of latent period exclusion, by excluding those who died, were diagnosed with AD within the first 2 or 4 years since the index date, or had a follow-up duration of less than 2 or 4 years.

Statistical analyses were performed using the R software, version 3.3.3 (R Foundation for Statistical Computing, Vienna, Austria) and SAS 9.4 version (SAS Inc., Cary, NC, USA). Two-sided p-values less than 0.05 were considered significant.

6. Data availability

The data used in this study were obtained from the NHIS database of South Korea. Access to these data is restricted and was granted under a specific license for the purposes of this study (NHIS-2023-1-373); therefore, the data are not publicly accessible. Data are available upon reasonable request, with review and approval from the National Health Insurance Service Research Committee (https://nhiss.nhis.or.kr/bd/ay/bdaya001iv.do).

RESULTS

1. Demographic and clinical characteristics

From 2010 to 2011, a total of 460,481 Koreans aged 66 years underwent the NSPTA. Following the study criteria and excluding participants with significant alcohol use, prior liver disease, dementia history, insufficient follow-up period or missing data, 376,902 participants were included in the analysis (Fig. 1). Baseline characteristics of the study participants are presented in Table 1. Males accounted for 41.5% of the study population and MASLD was present in 39.7%. Regarding the presence of MASLD and overweight/obese status (BMI ≥23 kg/m2), the proportions of overweight/obese non-MASLD, overweight/obese MASLD, lean non-MASLD, and lean MASLD were 30.4%, 37.0%, 29.9%, and 2.7%, respectively. Differences in characteristics according to the four categories are shown in Supplement Table 3.

Figure 1. Flowchart of study participant’s inclusion and exclusion.

Table 1

Characteristics of Study Participants.

Data are presented as number (%), mean±SD, or median (IQR)..

Q, quartile; MET, metabolic equivalent task; BP, blood pressure; eGFR, estimated glomerular filtration rate; KDSQ-P, Prescreening Korean Dementia Screening Questionnaire; FLI, fatty liver index; MASLD, metabolic dysfunction-associated steatotic liver diseasemean..

2. MASLD, overweight/obese, and the risk of AD

During the mean follow-up of 10.38±1.90 years, there were 23,874 individuals who received a new diagnosis of AD (6.33%). First, we analyzed the individual associations between MASLD, overweight/obese status, and the risk of AD. Compared with lean participants (BMI <23 kg/m2), overweight/obese participants (BMI ≥23 kg/m2) had a 0.92-fold lower risk of AD (adjusted hazard ratio [HR], 0.92; 95% confidence interval [CI], 0.88 to 0.96; p<0.001) (Supplementary Table 4, Model 1). When BMI was treated as a continuous variable, higher BMI was associated with a lower risk of AD (adjusted HR, 0.98; 95% CI, 0.97 to 0.98; p<0.001) (Table 2). Participants with MASLD had a higher risk for developing AD than those with non-MASLD (adjusted HR, 1.15; 95% CI, 1.11–1.18; p<0.001) (Supplementary Table 4, Model 2).

Table 2

Risk Factors for the Development of Alzheimer Disease.

Data regarding the development of Alzheimer disease were derived from the Cox proportional hazards regression model..

HR, hazard ratio; CI, confidence interval; Q, quartile; MET, metabolic equivalent task; LDL, low-density lipoprotein; KDSQ-P, Prescreening Korean Dementia Screening Questionnaire; BMI, body mass index; MASLD, metabolic dysfunction-associated steatotic liver disease..

*Adjusted for the variables listed in this table (sex, household income, smoking status, alcohol consumption, physical activity, chronic kidney disease, systolic blood pressure, fasting glucose, LDL cholesterol, KDSQ-P ≥4, hearing loss, BMI, and MASLD category..

We further assessed the risk of incident AD based on the presence of MASLD and overweight/obese status. Compared with individuals with overweight/obese non-MASLD (570.69 cases per 100,000 person-years; reference), the incidence rate of AD was higher in those with overweight/obese MASLD (601.99 cases per 100,000 person-years), lean non-MASLD (651.27 cases per 100,000 person-years), and lean MASLD (736.08 cases per 100,000 person-years) (Supplementary Table 4, Model 3). Cumulative incidence curves for the development of AD (Fig. 2) showed that the risk of AD differed across the groups (log-rank p<0.001). Multivariate Cox proportional regression model for the development of AD demonstrated that with the reference of overweight/obese non-MASLD, the adjusted HRs (95% CI) for lean MASLD, overweight/obese MASLD, and lean non-MASLD were 1.34 (1.24 to 1.45), 1.13 (1.09 to 1.17), and 1.08 (1.04 to 1.13), respectively (p<0.001).

Figure 2. Cumulative incidence curve for the development of Alzheimer disease. MASLD, metabolic dysfunction-associated steatotic liver disease.

The results of subgroup analyses according to the presence of advanced liver fibrosis (BARD score ≥2) in individuals with MASLD are shown in Table 3. In overweight/obese MASLD subjects, advanced liver fibrosis was associated with an increased risk of AD (adjusted HR, 1.08; 95% CI, 1.01 to 1.14; p=0.014). In lean MASLD subjects, the presence of advanced liver fibrosis also tended to be associated with an increased risk of incident AD (adjusted HR, 1.21; 95% CI, 0.99 to 1.48; p=0.070).

Table 3

Incidence of Alzheimer Disease Based on the Presence of Advanced Liver Fibrosis in MASLD.

MASLD, metabolic dysfunction-associated steatotic liver disease; HR, hazard ratio; CI, confidence interval..

*Adjusted for sex, household income, smoking status, alcohol consumption, physical activity, chronic kidney disease, systolic blood pressure, fasting glucose, low-density lipoprotein cholesterol, Prescreening Korean Dementia Screening Questionnaire (≥4), hearing loss, and body mass index..

The results of sensitivity analyses according to the presence of hepatic steatosis (FLI ≥30 or 60) regardless of the presence of metabolic abnormality are shown in Supplementary Table 4. When the presence of hepatic steatosis was defined as FLI ≥30, the incidence of AD was comparable between individuals without (610.32 cases per 100,000 person-years) and with hepatic steatosis (610.62 cases per 100,000 person-years). Multivariable Cox proportional regression model demonstrated that the presence of hepatic steatosis increased the risk for the development of AD (adjusted HR, 1.15; 95% CI, 1.11 to 1.18; p<0.001) (Supplementary Table 4, Model 4). When we divided the study participants into four groups according to the presence of hepatic steatosis (FLI ≥30) and overweight/obese (BMI ≥23 kg/m2) status, the risk of AD differed across the groups. Compared with individuals with overweight/obese non-hepatic steatosis (570.69 cases per 100,000 person-years; reference), the incidence rate of AD was higher in overweight/obese individuals with hepatic steatosis (601.99 cases per 100,000 person-years: adjusted HR, 1.13; 95% CI, 1.09 to 1.18, p<0.001), lean individuals without hepatic steatosis (651.53 cases per 100,000 person-years: adjusted HR, 1.08; 95% CI, 1.04 to 1.13; p<0.001), and lean individuals with hepatic steatosis (730.46 cases per 100,000 person-years: adjusted HR, 1.34; 95% CI, 1.23 to 1.44; p<0.001) (Supplementary Table 4, Model 5). When the presence of hepatic steatosis was defined as FLI ≥60, we also obtained similar results (Supplementary Table 4, Models 6 and 7).

The results of sensitivity analyses of latent period exclusion are shown in Supplementary Table 5. When we excluded the study participants who were lost to follow-up within 2 years (or 4 years) or diagnosis of AD within 2 years (or 4 years) since NSPTA examination, we still obtained consistent results that the order of increasing AD risk was lean MASLD (highest), overweight/obese MASLD, lean non-MASLD, and overweight/obese non-MASLD (lowest).

DISCUSSION

In the present study, we investigated the influence of both MASLD and BMI on the incidence of AD in the general population of South Korea. We constructed a retrospective cohort of 66-year-old dementia-free Korean participants using data from the national screening program, and reviewed the claims data from the NHIS to identify a new diagnosis of AD in these study participants. Our major findings were as follows: (1) during a mean follow-up of 10.38 years, 23,874 individuals (6.33%) aged 66 were newly diagnosed with AD; (2) participants with MASLD at baseline had a higher risk of developing AD than those without MASLD. Overweight/obese participants (BMI ≥23 kg/m2) had a 0.92-fold lower risk of AD than lean participants (BMI <23 kg/m2); (3) when we further assessed the risk of incident AD based on the presence of MASLD and overweight/obese status, the order of increasing AD risk was lean MASLD (highest), overweight/obese MASLD, lean non-MASLD, and overweight/obese non-MASLD (lowest); and (4) advanced liver fibrosis was associated with an increased risk of AD in overweight/obese individuals with MASLD. Taken together, our findings suggest that the impact of MASLD on AD risk becomes evident when individuals are stratified by BMI and that advanced liver fibrosis in MASLD is associated with an increased risk of developing AD.

Accumulating evidence indicates that MASLD and dementia share common biological mechanisms (i.e., insulin resistance, systemic inflammation, and prothrombotic state32, and can potentially affect each other, consistent with the emerging concept of liver-brain axis.33 Disruption of the gut microbiota,34 hyperammonemia,35 and smaller total cerebral brain volumes,36 which are often observed in individuals with MASLD, may also accelerate cognitive decline. Moreover, evidence suggests that the liver is involved in brain β-amyloid deposits as well as peripheral clearance of circulating β-amyloid in the blood,37 which may contribute to the pathogenesis of dementia. Despite this evidence, previous epidemiological studies investigating the association between MASLD and incident dementia have yielded inconsistent results, with some reporting that MASLD increases the risk of developing dementia,8-11 while others reported that MASLD is not associated with incident dementia12-15 or even lowers the risk of dementia.12 The discrepant findings across studies are partly explained by differences in the study populations, study designs, and operational definitions of MASLD and dementia. Another possible explanation is that the relationship between MASLD and the risk of dementia may vary depending on the subtype of MASLD and may require analyses by subtype. For example, although obesity and metabolic disorders are often interrelated in MASLD, they can also exist independently in some individuals (i.e., the lean MASLD subtype) with different clinical implications. This may be in line with the recently introduced concept of “metabolically healthy obesity” and “metabolically obese, but normal weight” to explain the heterogeneous nature of obesity.38

In the present study, individuals with MASLD had a higher risk of developing AD than those without MASLD in the overall cohort. When the study participants were further stratified based on their BMI status, we found that the presence of MASLD was associated with incident AD in the following order: lean MASLD (highest) > overweight/obese MASLD > lean non-MASLD > overweight/obese non-MASLD (lowest). It is well established that all components of metabolic abnormalities of MASLD (i.e., obesity, insulin resistance, hypertension, and dyslipidemia) increase the risk of developing dementia.18 Metabolic disorders in MASLD can lead to a variety of microvascular lesions, which subsequently accelerate the occurrence of cognitive decline and dementia.39 However, regarding the relationship between obesity and dementia, opposite results (i.e., obesity reduces the risk of dementia) have also been reported in many studies, consistent with the concept of the obesity paradox.19 Evidence suggests that BMI is determined by several factors that are closely linked to neurodegenerative processes in the preclinical stages of AD, including disturbed energy homeostasis, hypothalamic dysregulation and sarcopenia, as well as gastrointestinal dysfunction, impaired olfaction, and reduced motivation.40 Both high and low BMI can involve a series of pathological cellular responses leading to neurodegeneration in AD, via adiposity-related inflammation41 and low central insulin levels,42 respectively. One possible explanation for these mixed results across studies is that the association between BMI and AD risk is likely attributable to two separate processes in the development of AD:19,43 higher BMI in middle-aged individuals may be linked to an increased risk of AD, whereas lower BMI associated with preclinical metabolic changes in late life may be observed before AD diagnosis. In this study, we found that late-life overweight or obesity was associated with a lower risk of developing AD.44 In particular, our finding that the impact of MASLD on AD risk became more evident after stratification by BMI status suggests that being overweight or obese may be more strongly associated with the risk of developing AD than MASLD. Taken together, active screening for AD in underweight populations in later life could be a more efficient public health strategy for reducing the burden of AD. Early intervention for the metabolic comorbidities associated with MASLD may also be an effective therapeutic strategy for delaying the onset of AD.

We performed subgroup and sensitivity analyses to assess the relationship between liver integrity and the risk of AD. Hepatic steatosis, regardless of the presence of metabolic abnormalities, was associated with incident AD, and the presence of advanced liver fibrosis further strengthened the risk of AD in individuals with MASLD. It is well recognized that individuals with chronic liver disease or liver cirrhosis are more vulnerable to frailty compared to those without chronic liver disease,45 which may predispose to future cognitive decline and dementia.15 Experimental studies have shown that liver fibrosis is characterized by a proinflammatory state46 and shares a metabolic milieu of insulin resistance, adipokine secretion, and oxidative stress,47 facilitating the detrimental effects of cardiometabolic risk factors on the brain. Furthermore, some evidence suggests that advanced liver fibrosis is associated with AD-related pathological burden in the brain, independently of cardiometabolic risk factors.48 Our findings indicate that the liver itself plays a considerable role in the risk of incident dementia and timely intervention in liver dysfunction can help delay brain aging.

Our study has some limitations. First, hepatic steatosis and advanced liver fibrosis were defined using non-invasive scores without imaging or pathologic confirmation. However, these noninvasive methods have undergone extensive validation,27,49 and guidelines recognize the usefulness of noninvasive surrogates in large epidemiological studies.25,26 There may also be inaccuracies in the diagnosis of AD based on the national health claims data, as it was not originally created for research purposes. Second, it may be worth considering whether the relationship between MASLD and subsequent AD risk varies depending on the exposure duration or severity of MASLD, although this information was not available in the cross-sectional dataset from the NSPTA. Third, it may be oversimplifying to define overweight/obese status by categorizing the BMI based on a cutoff of 23 kg/m2. However, when we performed additional analyses by dividing the BMI categories as underweight/normal weight (BMI <23 kg/m2), overweight (23 kg/m2≤BMI<25 kg/m2), and obesity (BMI ≥25 kg/m2),50 we found that additional categorization of BMI using two cutoff values (i.e., 23 and 25 kg/m2) did not provide additional information on the risk for incident AD (Supplementary Table 4, Model 8). Furthermore, BMI may not be a good measure of adiposity in the elderly and may instead be an indicator of frailty.19 Muscle mass is also reflected in BMI, which can be protective against neurodegenerative processes.51,52 It may be cautiously hypothesized that lower BMI observed in lean MASLD subjects could reflect decreased muscle mass or sarcopenia, potentially increasing the AD risk identified in this group. Fourth, other potential risk factors for AD, such as apolipoprotein E genotyping and educational attainment, were not available for this nationwide cohort.

In conclusion, our findings suggest that the combination of normal/underweight BMI and MASLD synergistically increases the risk of developing AD. Lean MASLD was associated with a higher risk of AD than overweight/obese MASLD, indicating that the clinical significance of MASLD in relation to AD onset varies among the different MASLD subtypes. The interplay between MASLD, BMI, and AD risk is complex and warrants further prospective studies to evaluate the complex interactions among body composition, metabolic health, and neurodegenerative disorders, which might inform more effective interventions and preventive strategies in public health. In particular, our findings suggest the potential benefit of personalized clinical strategies for managing lean MASLD patients, emphasizing muscle preservation as well as weight management to potentially mitigate AD risk.

ACKNOWLEDGEMENTS

This study was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (grant number: RS-2024-00345524). This study was also supported by the Research Supporting Program of the Korean Association for the Study of the Liver and The Korean Liver Foundation (2024-31-0934).

MID (Medical Illustration & Design), as a member of the Medical Research Support Services of Yonsei University College of Medicine, providing excellent support with medical illustration.

CONFLICTS OF INTEREST

J.K.K. is an editorial board member of the journal but was not involved in the peer reviewer selection, evaluation, or decision process of this article. No other potential conflicts of interest relevant to this article were reported.

AUTHOR CONTRIBUTIONS

Study concept and design: T.S.L., S.J.C. Data acquisition: J.J., J.K. Data analysis and interpretation: J.J., J.K. Drafting of the manuscript: T.S.L., S.J.C. Critical revision of the manuscript for important intellectual content: J.K., J.K.K. Statistical analysis: J.J., J.K. Obtained funding: T.S.L., J.K. Administrative, technical, or material support: S.J.C. Study supervision: J.K., J.K.K. Approval of final manuscript: All authors.

DATA AVAILABILITY STATEMENT

Data analyzed in this study are available from the corresponding author upon reasonable request.

SUPPLEMENTARY MATERIALS

• Abstract
• INTRODUCTION
• MATERIALS AND METHODS
• RESULTS
• DISCUSSION
• ACKNOWLEDGEMENTS
• CONFLICTS OF INTEREST
• AUTHOR CONTRIBUTIONS
• DATA AVAILABILITY STATEMENT
• SUPPLEMENTARY MATERIALS

Fig 1.

Figure 1.Flowchart of study participant’s inclusion and exclusion.

Fig 2.

Figure 2.Cumulative incidence curve for the development of Alzheimer disease. MASLD, metabolic dysfunction-associated steatotic liver disease.

Table 1

Characteristics of Study Participants

Data are presented as number (%), mean±SD, or median (IQR).

Q, quartile; MET, metabolic equivalent task; BP, blood pressure; eGFR, estimated glomerular filtration rate; KDSQ-P, Prescreening Korean Dementia Screening Questionnaire; FLI, fatty liver index; MASLD, metabolic dysfunction-associated steatotic liver diseasemean.

Table 2

Risk Factors for the Development of Alzheimer Disease

Data regarding the development of Alzheimer disease were derived from the Cox proportional hazards regression model.

HR, hazard ratio; CI, confidence interval; Q, quartile; MET, metabolic equivalent task; LDL, low-density lipoprotein; KDSQ-P, Prescreening Korean Dementia Screening Questionnaire; BMI, body mass index; MASLD, metabolic dysfunction-associated steatotic liver disease.

*Adjusted for the variables listed in this table (sex, household income, smoking status, alcohol consumption, physical activity, chronic kidney disease, systolic blood pressure, fasting glucose, LDL cholesterol, KDSQ-P ≥4, hearing loss, BMI, and MASLD category.

Table 3

Incidence of Alzheimer Disease Based on the Presence of Advanced Liver Fibrosis in MASLD

MASLD, metabolic dysfunction-associated steatotic liver disease; HR, hazard ratio; CI, confidence interval.

*Adjusted for sex, household income, smoking status, alcohol consumption, physical activity, chronic kidney disease, systolic blood pressure, fasting glucose, low-density lipoprotein cholesterol, Prescreening Korean Dementia Screening Questionnaire (≥4), hearing loss, and body mass index.

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January 2026

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