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Genetic evidence for the liver-brain axis: lipid metabolism and neurodegenerative disease risk

Lipids in Health and Disease volume 24, Article number: 41 (2025) Cite this article

Abstract

Background

The liver‒brain axis is critical in neurodegenerative diseases (NDs), with lipid metabolism influencing neuroinflammation and microglial function. A systematic investigation of the genetic relationship between lipid metabolism abnormalities and ND, namely, Alzheimer's disease (AD), Parkinson's disease (PD), multiple sclerosis (MS), and amyotrophic lateral sclerosis (ALS), is lacking. To assess potential causal links between ND and six lipid parameters, two-sample Mendelian randomization (MR) was used.

Methods

Large-scale European ancestry GWAS data for lipid parameters and ND (AD, ALS, PD, and MS) were used. Genetic variants demonstrating significant correlations (P < 5 × 10−8) with lipid metabolism parameters were identified and employed as instrumental variables (IVs) after proper validation. The research incorporated UK Biobank genomic data to examine associations between genetic variants and lipid metabolism parameters. The analysis included primary MR, sensitivity analyses, and multivariable MR, which considered potential mediators.

Results

MR via the inverse-variance weighted method revealed causal effects of cholesterol (CHOL, OR = 1.10, 95% CI: 1.03–1.18, P = 4.23 × 10⁻3) and low-density lipoprotein cholesterol (LDLC, OR = 1.10, 95% CI: 1.03–1.17, P = 3.28 × 10⁻3) on the risk of ALS, which were validated across multiple methods. Potential correlations were observed between ApoB and ALS and inversely correlated with AD, whereas no significant associations were found for PD or MS. CHOL and LDLC associations with ALS demonstrated no significant heterogeneity or pleiotropy, supporting their reliability.

Conclusions

Higher CHOL and LDLC levels were associated with increased ALS risk, suggesting a potential causal link, and supporting the liver‒brain axis hypothesis in ND. Current genetic evidence does not support a significant role for lipid metabolism in PD and MS etiology, suggesting the relationship between lipid metabolism and other NDs may be more complex and warrants further investigation.

Introduction

Neurodegenerative diseases (NDs) of the central nervous system, including Alzheimer’s disease (AD), Parkinson’s disease (PD), multiple sclerosis (MS), and amyotrophic lateral sclerosis (ALS), affect human health and lifespan, with an increasing incidence rate worldwide. With the gradual loss of neurons with age, ND can lead to deterioration including cognitive performance, motor control, and other essential neurological functions [1]. Despite extensive research, the complete molecular and cellular pathways underlying neurodegenerative disorders remain incompletely elucidated. While advancing age represents a primary contributor to these diseases, multiple physiological elements and external environmental influences also contribute to disease manifestation [2, 3]. Recent studies have concluded that metabolic risk factors contribute to the occurrence and development of ND, suggesting promising strategies for prevention and treatment [4, 5].

Recent research has established the liver-brain axis as a crucial pathway in human health [6]. Studies have uncovered multiple molecular mechanisms underlying the liver-brain axis in neurodegeneration. For example, liver endogenic hormone FGF21, exerts neuroprotective effects by maintaining mitochondrial function via AMPKα/AKT signaling, reducing neuroinflammation through NF-κB pathway inhibition, and suppressing oxidative stress [7]. Also, liver-produced bile acids can cross the blood–brain barrier directly and activate nuclear receptors in neurons and glial cells, or through an indirect FXR-FGF15/19 and TGR5-GLP-1 signaling pathway to affect neuroinflammation and neurodegeneration [8]. Additionally, hepatic dysfunction of branched-chain amino acids metabolism impacts neurotransmitter synthesis and neuronal function, leading to neurotoxicity and cognitive decline [9].

Lipid metabolism in the liver, is related to the maintenance of body homeostasis, including ND. Neuroinflammation is considered a common pathophysiological mechanism of ND. Microglia in the brain are crucial for maintaining homeostasis and alleviating neuroinflammation, and lipid and lipoprotein metabolism are important metabolic processes that meet the bioenergetic needs of microglia to perform protective functions [3, 10]. Hence, as an important fuel for neurons in the brain, lipid metabolism has been associated with various NDs [11]. Previous placebo-crossover trials have shown that medium-chain triglycerides can significantly improve cognitive function and lipid metabolomics in patients with early to intermediate stage AD [12]. Both circulating and brain cholesterol levels are inversely linked with motor dysfunction in PD patients [13, 14]. However, studies also suggest an elevated PD risk in patients with high cholesterol [15, 16], and evidence from clinical trials indicates that omega-3 unsaturated fatty acids supplementation fails to improve AD patient outcomes, including cognitive, functional, and depressive symptom [17].

Given the conflicting research findings, the mechanistic links between specific lipid metabolism parameters and ND disease development remains controversial and warrants further investigation. To address this knowledge gap, this study employed Mendelian randomization to explore potential genetically defined causal relationships between lipid metabolism parameters and ND within the context of the liver‒brain axis. This approach allowed us to overcome some limitations of observational studies and provided more robust evidence for causal associations [18, 19]. Understanding these connections could have significant clinical implications for disease prevention and treatment strategies.

Methods

Study design

A two-sample Mendelian randomization (MR) was used to examine potential causal relationships between lipid metabolism and NDs. A schematic representation of the study and the analysis pipeline is shown in Fig. 1. Following the three fundamental MR assumptions, our study was structured as follows:

Fig. 1
figure 1

Study overview. Schematic representation of the Mendelian randomization (MR) study design and analysis pipeline

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Genetic Instrument Selection (Assumption 1: Relevance) Six well-established, clinically relevant lipid metabolites were selected (apolipoprotein A1 (ApoA), apolipoprotein B (ApoB), total cholesterol (CHOL), high-density lipoprotein cholesterol (HDLC), low-density lipoprotein cholesterol (LDLC), and triglycerides (TG)) as exposures based on their potential roles in NDs, as these metabolites are routinely measured in clinical settings and have been previously implicated in neurological disorders. Genetic variants from Genome-Wide Association Studies (GWAS) databases served as instrumental variables (IVs), those with strong associations to lipid metabolism were selected, rather than direct measurements from blood. The selection process applied strict significance thresholds (P < 5 × 10⁻⁸) and linkage disequilibrium (LD) clumping (r2 < 0.001, clumping window = 10,000 kb) to ensure independent signals. Single-nucleotide polymorphisms (SNPs) with F-statistic values > 10 were extracted from the GWAS datasets, followed by allele harmonization. The selection of instrumental variables (IVs) was performed independently for each unique exposure-outcome combination, ensuring that each pair had its own distinct set of genetic instruments.

The upper part illustrates the MR assumptions and potential relationships between SNPs, exposures (lipid metabolism measures), and outcomes (neurodegenerative diseases), with confounders (obesity and inflammation). The solid arrows indicate assumed causal relationships, whereas the dashed lines with red X symbols represent associations that should not exist to satisfy MR assumptions. The lower part outlines the step-by-step analysis process, from the GWAS dataset to multivariable MR analysis. Key elements include instrument selection criteria, primary and sensitivity MR analyses, and the final multivariable analysis adjusting for potential confounders. Specific lipid biomarkers, neurodegenerative diseases, and confounding factors are listed. MR: Mendelian randomization; SNPs: single-nucleotide polymorphisms; GWAS: genome-wide association studies; LD: linkage disequilibrium.

Confounder Check (Assumption 2: No Horizontal Pleiotropy) To address potential pleiotropy, our analysis incorporated several key confounding factors: body mass index (BMI), blood glucose, and inflammatory markers (C-reactive protein [CRP] and IL-17). Horizontal pleiotropy was evaluated via the MR‒Egger intercept test, and heterogeneity across causal effects was examined via the Cochran’s Q test and the Rucker Q’ test. To address any potential residual confounding from incomplete covariate adjustment, sensitivity analyses using MR-Egger regression could detect and account for potential pleiotropy arising from unmeasured confounders. The analysis also included albumin and gamma-glutamyl transpeptidase (GGT) as markers of liver function. All the data were sourced from publicly available GWAS databases.

Outcome Validation (Assumption 3: Exclusion Restriction) By focusing on these key lipid parameters as exposures, this study aimed to provide a targeted analysis of their potential causal relationships with four NDs as outcomes: Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), and multiple sclerosis (MS). The inverse variance weighted (IVW) method assessed causal effects between lipid parameters and NDs. All source GWAS datasets incorporated key adjustments to minimize potential confounding: the UK Biobank lipid parameters data were adjusted for age, sex, and the first 20 genetic principal components; the ND outcome datasets included similar demographic and genetic adjustments (age, sex, and principal components for ALS and PD; age and principal components for AD; sex and principal components for MS). Supporting analyses via MR Egger, weighted median, simple mode, and weighted mode were conducted. Sensitivity analyses incorporated the MR multidirectional residual sum with outliers (MR-PRESSO) to correct for outliers. Reverse MR was conducted by setting ND as the exposure and lipid metabolism parameters as the outcomes. A validation analysis was performed using a separate ND dataset. To ensure the use of the most up-to-date GWAS data, the recently published large-scale metabolite GWAS covering European and Asian population was incorporated as a validation dataset [20]. The associations between the lipid metabolites and NDs were reanalyzed using these updated summary statistics. To elucidate the potential influences of mediating factors on causal inference, a multifactorial MR analysis was conducted, considering four potential confounders: BMI, blood glucose, CRP, and IL-17. BMI and blood glucose were selected because of their likely associations with lipid metabolism abnormalities, whereas CRP and IL-17 were chosen because of their potential relevance to ND occurrence.

Data sources

European ancestry GWAS databases were utilized, prioritizing the most recent and comprehensive datasets. Lipid metabolism parameters were obtained from the UK Biobank. Data for AD, PD, and MS were sourced from various consortia and databases, including the International Genomics of Alzheimer's Project, the International Parkinson's Disease Genomics Consortium, the International Multiple Sclerosis Genetics Consortium, the FinnGen consortium, and the UK Biobank. ALS data were obtained through a comprehensive meta-analysis of GWAS published in recent literature. Table 1 presents details of the validation and mediator datasets. For AD, clinically diagnosed cases were included based on established diagnostic criteria. ALS cases were classified according to El Escorial Criteria. MS cases were clinically confirmed through standard diagnostic procedures. The PD dataset included both idiopathic and genetic forms of the disease, as this distinction was not made in the original GWAS data. Detailed demographic information for the study populations of AD, PD, MS, and ALS can be found in Supplementary file 1.

Table 1 Data sources for exposures, neurodegenerative diseases, and confounders of interest
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Statistical analysis

All analyses used R version 4.2.2 (RStudio, USA). The two-sample MR package for Mendelian randomization and the MVMR package for multifactorial MR were employed. To account for multiple testing across six lipid parameters and ND, Bonferroni correction (P < 0.0083 [0.05/6]) was applied. This stringent threshold aims to minimize false-positives. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines, ensuring transparent and comprehensive reporting, methodological rigor, and facilitating result interpretation.

Results

Associations between lipid parameters and NDs

Firstly the potential association between hepatic function and NDs was investigated by analyzing serum albumin and GGT. No significant associations were observed (Supplementary Table 1). Then the relationships between six lipid metabolism parameters were illustrated, namely, ApoA, ApoB, CHOL, HDLC, LDLC, and TG, and the occurrence of ND in Fig. 2. IVW analysis revealed a significant association between high CHOL levels and ALS incidence (OR = 1.10, 95% CI: 1.03–1.18, P = 4.23 × 10⁻3). This association was consistently validated via two additional MR methods: weighted median (OR = 1.11, P = 4.03 × 10⁻2) and weighted mode (OR = 1.11, P = 4.78 × 10⁻2) (Supplementary Table 2). Additionally, heightened LDLC levels were significantly correlated with the incidence of ALS (OR = 1.10, 95% CI: 1.03–1.17, P = 3.28 × 10⁻3), with consistent validation across the weighted median (OR = 1.12, P = 9.78 × 10⁻3), weighted mode (OR = 1.12, P = 1.23 × 10⁻2), and MR Egger methods (OR = 1.12, P = 2.27 × 10⁻2) (Supplementary Table 2). Furthermore, a potential correlation was observed between ApoB and increased ALS incidence (OR = 1.08, 95% CI: 1.02–1.15, P = 0.016), although the significance diminished after Bonferroni correction. Conversely, a potential inverse association between ApoB and AD incidence was noted (OR = 0.91, 95% CI: 0.85–0.99, P = 0.024), yet this association also did not survive Bonferroni correction. The number of SNPs selected at each step of the IV selection process (e.g., initial SNPs, after LD clumping, after harmonization, after confounder checks) was shown in Supplementary Table 3.

Fig. 2
figure 2

Forest plot of lipid markers and neurodegenerative disease associations

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The plot displays odds ratios (ORs) with 95% confidence intervals (CIs) for the effects of ApoA, ApoB, CHOL, HDLC, LDLC, and TG on Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), and Parkinson's disease (PD). Each row represents a specific lipid‒disease association, with the number of SNPs used as instruments (Nsnp), OR, 95% CI, and P value. Significant associations with P < 0.05 and P < 0.0083 (0.05/6) are highlighted with single and double asterisks, respectively. The forest plot on the right visually represents the ORs and their confidence intervals. The orange squares highlight associations with P < 0.05, and the red squares highlight associations with P < 0.0083.

In the context of PD and MS, no discernible associations were identified with lipid metabolism markers. Despite the use of alternative MR analysis methods, including MR Egger, weighted median, simple mode, and weighted mode, and even without multiple comparison corrections, no statistically significant results emerged (Supplementary Table 2).

Importantly, in the assessment of heterogeneity via the Egger and IVW methods, as well as the detection of horizontal pleiotropy via the Egger method, many of the aforementioned MR results displayed heterogeneity and/or pleiotropy (Supplementary Table 4). For example, the relationship between ApoB and AD exhibited significant heterogeneity (Egger Q = 88.3, P = 3.83 × 10⁻5). Importantly, in the evaluation of the associations between CHOL and LDLC with ALS, no significant heterogeneity was detected (CHOL for ALS: heterogeneity Egger P = 0.203; LDLC for ALS: heterogeneity Egger P = 0.355), with no pleiotropy detected (CHOL for ALS: pleiotropy Egger P = 0.252; LDLC for ALS: pleiotropy Egger P = 0.610), supporting the reliability of the aforementioned results.

Sensitivity analysis

MR-PRESSO were applied to identify and exclude outliers with heterogeneity or pleiotropy, reanalyzing the data to yield the outlier-corrected results presented in Fig. 3A. The results revealed that the association between CHOL and ALS remained significant (OR = 1.10, 95% CI: 1.03–1.18, P = 6.14 × 10⁻3), and similarly, the relationship between LDLC and ALS remained significant (OR = 1.10, 95% CI: 1.03–1.17, P = 5.56 × 10⁻3).

Fig. 3
figure 3

Mendelian randomization results with outlier correction and secondary dataset validation

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(A) Forest plot showing outlier-corrected OR results from MR-PRESSO analysis for associations between lipid metabolism markers and neurodegenerative diseases. (B) Forest plot displaying validation dataset-OR results when an independent dataset was used to validate associations between lipid metabolism markers and neurodegenerative diseases. Both plots present odds ratios (ORs) with 95% confidence intervals (CIs) for the effects of ApoA, ApoB, CHOL, HDLC, LDLC, and TG on AD, ALS, MS, and PD. Each row represents a specific lipid‒disease association, including the OR, 95% CI, and P value. Significant associations with P < 0.05 and P < 0.0083 (0.05/6) are highlighted with single and double asterisks, respectively. The forest plots visually depict the ORs and their confidence intervals, allowing easy comparison of effect sizes and significance across different lipid‒disease relationships in both the outlier-corrected and validation analyses. The orange squares highlight associations with P < 0.05, and the red squares highlight associations with P < 0.0083.

Subsequently, by employing a reverse MR approach to investigate potential biases induced by reverse causation, ALS was considered as the exposure factor, exploring its impact on CHOL and LDLC. The results indicated a lack of significant effects of ALS on CHOL and LDLC (CHOL: OR = 0.93, 95% CI: 0.79–1.10, P = 0.411; LDLC: OR = 0.94, 95% CI: 0.82–1.08, P = 0.392, IVW approach), with no observed heterogeneity or pleiotropy (Supplementary Table 5).

Finally, to validate these findings, the latest independent metabolite GWAS dataset was used and the associations between CHOL and LDLC with ALS remained significant in this validation analysis (Fig. 3B), reinforcing result reliability. The overall results closely mirrored the primary analysis, with the association between CHOL and ALS remaining significant (OR = 1.11, 95% CI: 1.03–1.20, P = 7.28 × 10⁻3), and similarly, the relationship between LDLC and ALS maintaining significance (OR = 1.08, 95% CI: 1.01–1.16, P = 3.67 × 10⁻2). These sensitivity analyses enhanced the robustness of the primary results, providing additional support for the observed associations between lipid metabolism markers and ALS.

Multivariable MR

Multivariable MR-IVW analysis assessed CHOL and LDLC effects on ALS while adjusting for potential confounders. Four mediator factors were included, including BMI and glucose, both of which are associated with metabolism, and CRP and IL-17, which are linked to inflammatory responses. Throughout the adjustment for these factors, the direct effects of CHOL and LDLC on ALS remained unaltered (Fig. 4). This MVMR-IVW analysis strengthened the findings by accounting for confounders and confirming independent lipid-ALS associations.

Fig. 4
figure 4

Multifactorial Mendelian randomization results

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(A) Forest plot showing multivariable Mendelian randomization results for the association of CHOL with ALS risk, adjusting for potential confounders. (B) Forest plot displaying multivariable Mendelian randomization results for the association of LDLC with ALS risk, adjusted for potential confounders. Both plots present odds ratios (ORs) with 95% confidence intervals (CIs) for CHOL or LDLC and each confounder (BMI, blood glucose, CRP, and IL-17) in separate models. Each row shows the number of SNPs used, OR, 95% CI, and P value for the lipid marker and the confounder. Statistically significant associations (P < 0.0083) are indicated by double asterisks and highlighted in red. The forest plots visually represent the ORs and their confidence intervals, demonstrating that both CHOL and LDLC maintain significant positive associations with ALS risk even after adjusting for these potential confounders, whereas the confounders themselves are not significantly associated with ALS risk.

Discussion

This two-sample MR study found associations between CHOL and LDLC levels and ALS risk. The causal associations of higher CHOL and LDLC levels with ALS were confirmed, suggesting potential genetically defined predictors of ALS.

The liver‒brain axis has emerged as a crucial pathway in the pathogenesis of various diseases, including ND [21]. Recent research has provided substantial evidence for the role of the liver‒brain axis in ND. The liver can regulate brain Aβ levels [22], while hepatic epoxide hydrolase activity affects brain Aβ and cognition [23]. The liver‒brain axis also influences body metabolism and behavior, especially by linking chronic liver inflammation to alterations in neural transmission and behavioral changes [21, 24]. Mounting evidence suggests a significant role for the liver in ND, with lipid metabolism serving as a key mediator. Lipid parameters have been implicated in the pathophysiology of several neurodegenerative conditions. In AD, altered cholesterol metabolism and oxidized lipids correlate with disease severity [25]. For ALS, lipid dysregulation has been linked to neuroinflammation and motor neuron degeneration [26]. In PD, specific lipid species alterations have been identified in both serum and CSF, suggesting potential biomarkers and therapeutic targets [27]. Research on MS has revealed associations between lipid peroxidation products and disease activity, as well as changes in cholesterol metabolites reflecting myelin damage [28]. Additionally, Liver X Receptors function as oxysterol sensors in the liver, receiving oxysterol signaling to regulate cholesterol transport and lipid synthesis for metabolic homeostasis. These oxysterols have emerged as critical mediators in neurodegenerative pathogenesis, mediating processes including amyloid-beta accumulation in AD [29], metabolic alterations in PD development [30], neuroinflammation in MS [31], and oxidative stress in ALS [32]. The accumulating evidence across these ND underscores the importance of further investigating the causal relationships between lipid metabolism parameters and neurodegeneration, potentially opening new avenues for diagnosis, prognosis, and therapeutic interventions.

Results show that elevated levels of CHOL and LDLC are both associated with ALS. The pathogenesis of ALS involves multiple biological processes, including protein misfolding, neuroinflammation, oxidative stress, and metabolic imbalance [33,34,35]. ALS patients typically exhibit a hypermetabolic state, with high metabolism associated with poorer prognosis [36, 37]. Lipid metabolism, as a crucial energy source for neuronal function, is likely an important risk factor for ALS. Research has identified several ALS-related genes and protein targets associated with cholesterol metabolism. For example, RNA/DNA-binding protein fused in sarcoma (FUS) and ATPase valosin-containing protein (VCP)/p97 regulate the rate-limiting enzyme HMGCR in cholesterol metabolism [38, 39], whereas ATXN2 and TDP-43 directly affect cholesterol levels [40, 41]. However, there have been controversial conclusions. Some studies have shown an increase in TC levels in ALS patients; however, this conclusion may have been biased due to the lack of a control group or the small sample size [42,43,44]. Even in studies with control groups and large sample sizes, scholars have reached varying conclusions regarding changes in CHOL levels in ALS patients [45,46,47,48]. Also, previous studies report conflicting LDLC-ALS associations: small sample meta-analyses found no significant relationship [49], while MR studies suggested LDLC mediates polyunsaturated fatty acids (PUFAs)-related traits to ALS risk [50]. These discrepancies may result from lacking control groups, small samples, and varying methods in collection, diagnosis, and analysis. Additionally, confounding factors and reverse causality can bias observational studies. While large-scale randomized clinical trials (RCTs) could address these issues, their development is often limited by practical constraints. By employing genetic variables as substitutes, MR revealed the associations between CHOL and LDLC levels and ALS and avoid the influence of bias on the conclusions. Consistent with the conclusions of this study, recent MR analyses of ALS risk loci also revealed a causal relationship between ALS and high cholesterol levels [51], which further strengthen the interpretation of the result.

While this study primarily highlighted the significant associations between CHOL/LDLC and ALS, a marginally significant association between ApoB and ALS was also observed. Although this association did not survive Bonferroni correction, it merits attention given ApoB's established role as the principal protein component of LDL particles [52, 53]. This finding aligns with the stronger associations between LDLC and ALS, as ApoB serves as a key indicator of LDL particle number and metabolism [54]. The marginally significant nature of the ApoB association might reflect the complex relationship between different components of lipid metabolism in neurodegeneration. Recent studies have suggested that alterations in lipid metabolism, including changes in ApoB-containing lipoproteins, may contribute to ALS pathogenesis through mechanisms involving cholesterol transport and cellular lipid homeostasis [55]. However, given the marginal statistical significance, this relationship requires further investigation in larger cohorts and through mechanistic studies to fully understand its biological relevance in ALS pathogenesis.

Findings of this study complement and extend recent Mendelian randomization studies examining metabolite-ND relationships. Notably, Lord et al. [56] and Huang et al. [57] conducted comprehensive metabolome-wide MR studies focusing on blood circulating metabolites and AD. Huang et al. investigated 123 circulating metabolites, while Lord et al. examined 19 metabolites previously linked to midlife cognition, both revealing significant associations between various metabolites and AD risk. While this study specifically focused on lipid metabolism parameters and uniquely incorporated multivariable MR analysis to account for confounding factors including BMI, blood glucose, and inflammatory markers. Through this targeted approach, combined with validation using independent datasets and consideration of the liver-brain axis, specific causal relationships were identified between lipid parameters and NDs, especially covering four NDs: AD, PD, ALS, and MS. Metabolome-wide MR studies also identified distinct metabolic profiles in MS, with serine, lysine, and ketone bodies (acetone and acetoacetate) showing risk-increasing effects, while the relationship with lipids appears more complex – total cholesterol and phospholipids showed opposing effects depending on their lipoprotein carriers [58]. This complexity in lipid metabolism's role across different NDs is further highlighted by the finding of no significant associations between lipid parameters and MS, suggesting disease-specific metabolic pathways. These collective findings underscore the importance of disease-specific approaches when investigating metabolic factors in neurodegeneration, as causal metabolic pathways may vary substantially across different neurodegenerative conditions.

In this study, among the six lipid metabolism parameters, only ApoB showed a near-significant association with AD risk. This finding aligns with the Mendelian randomization study conducted on 329,896 UK Biobank participants, which reported that only ApoB was significantly associated with increased dementia risk [59]. Interestingly, results revealed potentially opposing relationships for apolipoproteins: elevated ApoA levels and decreased ApoB levels might be linked to AD occurrence. This presents a paradox, as ApoA is typically considered the primary lipoprotein of HDLC with protective effects against atherosclerosis and coronary heart disease [52, 60], whereas ApoB, which is predominantly associated with LDLC, is generally viewed as a risk factor for these conditions [52, 53, 61]. Consistent with this study, a recent report from the European Alzheimer's & Dementia Biobank Mendelian Randomization Collaboration identified an association between high HDLC concentrations and increased AD risk [62]. The proposed mechanism involves high plasma HDLC disrupting homeostasis between plasma and cerebrospinal fluid apoE/apoA1 HDL particles. These conflicting results underscore the current controversy surrounding the relationship between HDLC, its associated apolipoprotein ApoA1, and AD risk, highlighting the need for further clinical data analysis. Additionally, this study revealed contrasting findings for ApoB in AD and ALS patients. Previous research on early-onset AD patients revealed no significant difference in plasma LDLC levels between carriers and noncarriers of APOB coding variants, whereas a strong association between high LDLC levels and early-onset AD was found in noncarriers [63], suggesting that factors beyond ApoB contribute to plasma LDLC differences in AD. Consequently, the association between ApoB and ND may be influenced by multiple factors, and more definitive evidence is needed before ApoB can be considered a reliable predictor of ND risk.

Findings of specific associations between CHOL/LDLC levels and ALS suggest that therapeutic strategies targeting lipid metabolism may be particularly relevant for ALS. However, this specificity also implies that such approaches may need to be tailored differently for other NDs. The observed lack of genetic associations with PD and MS does not rule out the involvement of lipid metabolism in these conditions but suggests that different metabolic pathways or mechanisms may be involved. This highlights the importance of developing disease-specific therapeutic strategies rather than applying a one-size-fits-all approach to NDs.

In this study, no significant associations between lipid metabolism parameters and MS or PD were observed. MS, which is characterized primarily by neuroinflammation and demyelination, has been linked to alterations in lipid metabolism. As demyelination occurs, cholesterol from neuronal myelin may induce changes in circulating lipids, with clinical cohort evidence suggesting associations between MS progression and elevated levels of LDL, total cholesterol, and triglycerides [28, 64,65,66]. However, despite indications that lipid metabolism levels in MS patients may aid in disease assessment, robust evidence supporting a causal relationship between lipid metabolism abnormalities and MS pathogenesis remains elusive. Similarly, while PD research has demonstrated connections to lipid metabolism, with changes in lipid composition potentially inducing protein alterations, such as α-synuclein accumulation, and some PD-associated genes (e.g., GBA and LRRK2) are thought to be involved in lipid metabolic processes [67,68,69,70,71], direct evidence for a causal relationship between lipid metabolism and PD onset is lacking. Results are consistent with other Mendelian randomization studies, which suggest that alterations in lipid metabolism do not play a prominent role in PD etiology [72, 73]. These results underscore the complexity of the liver‒brain axis in ND and highlight the need for further research to elucidate the precise mechanisms linking lipid metabolism to ND.

Results also revealed an intriguing pattern: while specific lipid metabolism parameters showed significant associations with NDs, general liver function markers (albumin and GGT) demonstrated no significant correlations. This discrepancy might be explained by the highly specific nature of liver-brain axis interactions in neurodegeneration. The brain, as one of the most lipid-rich organs, requires specific lipid metabolic pathways for myelin maintenance and synaptic function [74, 75], rather than depending on general liver synthetic capacity. This specificity is further supported by evidence that neurodegenerative pathologies often involve disruption of specific metabolic pathways rather than global liver dysfunction. For instance, cholesterol metabolism specifically influences synaptic plasticity and neurotransmitter release [76], and specific lipid-mediated signaling pathways, rather than general liver function, play crucial roles in neuroinflammation and neurodegeneration [77, 78]. This pathway specificity explains why alterations in particular lipid parameters, rather than general liver function markers, may serve as more relevant indicators of ND risk and progression.

Strengths and limitation

This study employed multiple MR methods (IVW, MR Egger, weighted median, and weighted mode) to enhance the reliability of findings and assess potential biases such as pleiotropy and heterogeneity. Comprehensive sensitivity analyses were performed, including MR-PRESSO for outlier detection and correction, and reverse MR analysis, which strengthened the robustness of the results. The multivariable MR approach accounted for potential confounding factors including BMI, blood glucose, CRP, and IL-17, minimizing possible bias in causal inference. Large-scale GWAS data from European populations were utilized, providing substantial statistical power for detecting causal relationships. The study adhered strictly to the STROBE guidelines, ensuring transparent and comprehensive reporting of methods and results.

Several limitations of this study should be acknowledged. Although genetic variants from GWAS data were used as instruments, the findings may still be affected by weak instrument bias, requiring careful balance between statistical power and potential pleiotropy in future studies. Second, while our datasets included Asian participants in both primary (2,407 ALS cases and 11,775 controls) and validation analyses, our study population remained predominantly of European genetic background, potentially limiting result generalizability. Genetic variants associated with lipid metabolism and neurodegenerative diseases can vary significantly across different populations due to distinct genetic architectures and environmental factors. Future studies incorporating multi-ethnic GWAS data are needed to validate these findings across diverse populations and establish whether these causal relationships are consistent across different ethnic backgrounds. Further research may be needed to validate these results in diverse populations. Third, the cross-sectional nature of the severity data for ND may have limited the ability to identify causal links between lipid-related traits and disease progression. Longitudinal studies may help better elucidate lipid metabolism's impact on disease progression and validate cross-sectional severity measures. Fourth, the potential lack of harmonization in analytical methods used for measuring lipid parameters across different GWAS datasets may introduce methodological heterogeneity. As these data were collected from various sources and potentially analyzed using different approaches, systematic variations in the measurements of cholesterol, triglycerides, LDL, HDL, ApoA and ApoB levels might exist. This methodological issue should be considered when interpreting the results. Additionally, the idiopathic and genetic forms of PD is unable to distinguish in this analysis, as this differentiation was not available in the original GWAS datasets. This combined analysis of different PD subtypes may have masked potential subtype-specific associations between lipid metabolism parameters and PD risk, suggesting that future studies should aim to analyze these forms separately when such data becomes available. Although the source GWAS datasets included adjustments for key demographic and genetic confounders, the degree of adjustment varied across studies. While the MR framework helps protect against confounding through the use of genetic instruments, residual bias from differential adjustment of covariates cannot be completely ruled out. However, sensitivity analyses using MR-Egger and multivariable approaches suggest that the main findings are robust to potential unmeasured confounding. While the MR analysis supports a causal role for elevated cholesterol in ALS, MR cannot determine the exact biological pathways driving the observed relationships. Further research using complementary designs can help confirm these findings and elucidate the underlying pathways.

Conclusions

In this two-sample MR study, higher CHOL and LDLC levels were associated with increased ALS risk, suggesting cholesterol as a genetically-linked risk factor for ALS and providing rationale for lipid profile monitoring and lipid-modifying interventions in ALS management. Additionally, potential associations between ApoB and both ALS and AD risk were observed, but more exploration is required to clarify the biological pathways involved before clinical translation.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

ND:

Neurodegenerative diseases

AD:

Alzheimer's disease

PD:

Parkinson's disease

MS:

Multiple sclerosis

ALS:

Amyotrophic lateral sclerosis

MR:

Mendelian randomization

IVs:

Instrumental variables

ApoA:

Apolipoprotein A1

ApoB:

Apolipoprotein B

CHOL:

Total cholesterol

HDLC:

High-density lipoprotein cholesterol

LDLC:

Low-density lipoprotein cholesterol

TG:

Triglycerides

GGT:

Gamma-glutamyl transferase

GWAS:

Genome-Wide Association Studies

SNPs:

Single-nucleotide polymorphisms

LD:

Linkage Disequilibrium

IVW:

Inverse variance weighted

STROBE:

Strengthening the Reporting of Observational Studies in Epidemiology

MR-PRESSO:

MR multidirectional residual sum with outliers

OR:

Odds ratios

CI:

Confidence intervals

FUS:

Fused in sarcoma

VCP:

ATPase valosin-containing protein

HMGCR:

3-Hydroxy-3-methylglutaryl-coenzyme A reductase

PUFA:

Polyunsaturated fatty acids

RCT:

Randomized clinical trials

BMI:

Body mass index

CRP:

C-reactive protein

IL-17:

Interleukin-17

References

  1. Hou Y, Dan X, Babbar M, Wei Y, Hasselbalch SG, Croteau DL, et al. Ageing as a risk factor for neurodegenerative disease. Nat Rev Neurol. 2019;15:565–81.

    Article  PubMed  Google Scholar 

  2. Brown RC, Lockwood AH, Sonawane BR. Neurodegenerative diseases: an overview of environmental risk factors. Environ Health Perspect. 2005;113:1250–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Kwon HS, Koh S-H. Neuroinflammation in neurodegenerative disorders: the roles of microglia and astrocytes. Translational Neurodegeneration. 2020;9. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7689983/ Cited 2024 Apr 1

  4. Teleanu DM, Niculescu A-G, Lungu II, Radu CI, Vladâcenco O, Roza E, et al. An overview of oxidative stress, neuroinflammation, and neurodegenerative diseases. Int J Mol Sci. 2022;23:5938.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Johnson AA, Stolzing A. The role of lipid metabolism in aging, lifespan regulation, and age-related disease. Aging Cell. 2019;18:e13048.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Yan M, Man S, Sun B, Ma L, Guo L, Huang L, et al. Gut liver brain axis in diseases: the implications for therapeutic interventions. Signal Transduct Target Ther. 2023;8:443.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Kang K, Xu P, Wang M, Chunyu J, Sun X, Ren G, et al. FGF21 attenuates neurodegeneration through modulating neuroinflammation and oxidant-stress. Biomed Pharmacother. 2020;129:110439.

    Article  CAS  PubMed  Google Scholar 

  8. Xing C, Huang X, Wang D, Yu D, Hou S, Cui H, et al. Roles of bile acids signaling in neuromodulation under physiological and pathological conditions. Cell Biosci. 2023;13:106.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Sperringer JE, Addington A, Hutson SM. Branched-Chain Amino Acids and Brain Metabolism. Neurochem Res. 2017;42:1697–709.

    Article  CAS  PubMed  Google Scholar 

  10. Loving BA, Bruce KD. Lipid and Lipoprotein Metabolism in Microglia. Front Physiol. 2020;11. Available from: https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2020.00393/full. Cited 2024 Apr 1

  11. Dawson G. Measuring brain lipids. Biochimica et Biophysica Acta (BBA) - Mol Cell Biol Lipids. 2015;1851:1026–39.

    Article  CAS  Google Scholar 

  12. Xu Q, Zhang Y, Zhang X, Liu L, Zhou B, Mo R, et al. Medium-chain triglycerides improved cognition and lipid metabolomics in mild to moderate Alzheimer’s disease patients with APOE4−/−: A double-blind, randomized, placebo-controlled crossover trial. Clin Nutr. 2020;39:2092–105.

    Article  CAS  PubMed  Google Scholar 

  13. Sterling NW, Lichtenstein M, Lee E-Y, Lewis MM, Evans A, Eslinger PJ, et al. Higher plasma LDL-cholesterol is associated with preserved executive and fine motor functions in Parkinson’s Disease. Aging Dis. 2016;7:237–45.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Huang X, Sterling NW, Du G, Sun D, Stetter C, Kong L, et al. Brain cholesterol metabolism and Parkinson’s disease. Mov Disord. 2019;34:386–95.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Benn M, Nordestgaard BG, Frikke-Schmidt R, Tybjærg-Hansen A. Low LDL cholesterol, PCSK9 and HMGCR genetic variation, and risk of Alzheimer’s disease and Parkinson’s disease: Mendelian randomisation study. BMJ. 2017;357:j1648.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Hu G, Antikainen R, Jousilahti P, Kivipelto M, Tuomilehto J. Total cholesterol and the risk of Parkinson disease. Neurology. 2008;70:1972–9.

    Article  CAS  PubMed  Google Scholar 

  17. Lin P-Y, Cheng C, Satyanarayanan SK, Chiu L-T, Chien Y-C, Chuu C-P, et al. Omega-3 fatty acids and blood-based biomarkers in Alzheimer’s disease and mild cognitive impairment: A randomized placebo-controlled trial. Brain Behav Immun. 2022;99:289–98.

    Article  CAS  PubMed  Google Scholar 

  18. Hemani G, Bowden J, Davey SG. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018;27:R195-208.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23:R89-98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Karjalainen MK, Karthikeyan S, Oliver-Williams C, Sliz E, Allara E, Fung WT, et al. Genome-wide characterization of circulating metabolic biomarkers. Nature. 2024;628:130–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Matsubara Y, Kiyohara H, Teratani T, Mikami Y, Kanai T. Organ and brain crosstalk: The liver-brain axis in gastrointestinal, liver, and pancreatic diseases. Neuropharmacology. 2022;205:108915.

    Article  CAS  PubMed  Google Scholar 

  22. Xiang Y, Bu X-L, Liu Y-H, Zhu C, Shen L-L, Jiao S-S, et al. Physiological amyloid-beta clearance in the periphery and its therapeutic potential for Alzheimer’s disease. Acta Neuropathol. 2015;130:487–99.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wu Y, Dong J-H, Dai Y-F, Zhu M-Z, Wang M-Y, Zhang Y, et al. Hepatic soluble epoxide hydrolase activity regulates cerebral Aβ metabolism and the pathogenesis of Alzheimer’s disease in mice. Neuron. 2023;111:2847-2862.e10.

    Article  CAS  PubMed  Google Scholar 

  24. D’Mello C, Swain MG. Liver-brain inflammation axis. Am J Physiol Gastrointest Liver Physiol. 2011;301:G749-761.

    Article  PubMed  Google Scholar 

  25. Giubilei F, Antonini G, Di Legge S, Sormani MP, Pantano P, Antonini R, et al. Blood cholesterol and MRI activity in first clinical episode suggestive of multiple sclerosis. Acta Neurol Scand. 2002;106:109–12.

    Article  CAS  PubMed  Google Scholar 

  26. Ketabforoush AHME, Chegini R, Barati S, Tahmasebi F, Moghisseh B, Joghataei MT, et al. Masitinib: the promising actor in the next season of the Amyotrophic Lateral Sclerosis treatment series. Biomed Pharmacother. 2023;160:114378.

    Article  CAS  PubMed  Google Scholar 

  27. Galper J, Dean NJ, Pickford R, Lewis SJG, Halliday GM, Kim WS, et al. Lipid pathway dysfunction is prevalent in patients with Parkinson’s disease. Brain. 2022;145:3472–87.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Quintana FJ, Yeste A, Weiner HL, Covacu R. Lipids and lipid-reactive antibodies as biomarkers for multiple sclerosis. J Neuroimmunol. 2012;248:53–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Infante J, Rodríguez-Rodríguez E, Mateo I, Llorca J, Vázquez-Higuera JL, Berciano J, et al. Gene-gene interaction between heme oxygenase-1 and liver X receptor-beta and Alzheimer’s disease risk. Neurobiol Aging. 2010;31:710–4.

    Article  CAS  PubMed  Google Scholar 

  30. Doria M, Maugest L, Moreau T, Lizard G, Vejux A. Contribution of cholesterol and oxysterols to the pathophysiology of Parkinson’s disease. Free Radic Biol Med. 2016;101:393–400.

    Article  CAS  PubMed  Google Scholar 

  31. Mutemberezi V, Buisseret B, Masquelier J, Guillemot-Legris O, Alhouayek M, Muccioli GG. Oxysterol levels and metabolism in the course of neuroinflammation: insights from in vitro and in vivo models. J Neuroinflammation. 2018;15:74.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Vejux A, Namsi A, Nury T, Moreau T, Lizard G. Biomarkers of amyotrophic lateral sclerosis: current status and interest of oxysterols and phytosterols. Front Mol Neurosci. 2018;11:12.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Mejzini R, Flynn LL, Pitout IL, Fletcher S, Wilton SD, Akkari PA. ALS Genetics, Mechanisms, and Therapeutics: Where Are We Now? Front Neurosci. 2019;13:1310.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Al-Chalabi A, Hardiman O. The epidemiology of ALS: a conspiracy of genes, environment and time. Nat Rev Neurol. 2013;9:617–28.

    Article  CAS  PubMed  Google Scholar 

  35. Ling S-C, Polymenidou M, Cleveland DW. Converging mechanisms in ALS and FTD: disrupted RNA and protein homeostasis. Neuron. 2013;79:416–38.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Bouteloup C, Desport J-C, Clavelou P, Guy N, Derumeaux-Burel H, Ferrier A, et al. Hypermetabolism in ALS patients: an early and persistent phenomenon. J Neurol. 2009;256:1236–42.

    Article  CAS  PubMed  Google Scholar 

  37. Desport JC, Preux PM, Magy L, Boirie Y, Vallat JM, Beaufrère B, et al. Factors correlated with hypermetabolism in patients with amyotrophic lateral sclerosis. Am J Clin Nutr. 2001;74:328–34.

    Article  CAS  PubMed  Google Scholar 

  38. Guzman KM, Brink LE, Rodriguez-Bey G, Bodnar RJ, Kuang L, Xing B, et al. Conditional depletion of Fus in oligodendrocytes leads to motor hyperactivity and increased myelin deposition associated with Akt and cholesterol activation. Glia. 2020;68:2040–56.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Morris LL, Hartman IZ, Jun D-J, Seemann J, DeBose-Boyd RA. Sequential actions of the AAA-ATPase valosin-containing protein (VCP)/p97 and the proteasome 19 S regulatory particle in sterol-accelerated, endoplasmic reticulum (ER)-associated degradation of 3-hydroxy-3-methylglutaryl-coenzyme a reductase. J Biol Chem. 2014;289:19053–66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Canet-Pons J, Sen N-E, Arsović A, Almaguer-Mederos L-E, Halbach MV, Key J, et al. Atxn2-CAG100-KnockIn mouse spinal cord shows progressive TDP43 pathology associated with cholesterol biosynthesis suppression. Neurobiol Dis. 2021;152:105289.

    Article  CAS  PubMed  Google Scholar 

  41. Ho WY, Chang J-C, Lim K, Cazenave-Gassiot A, Nguyen AT, Foo JC, et al. TDP-43 mediates SREBF2-regulated gene expression required for oligodendrocyte myelination. J Cell Biol. 2021;220:e201910213.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Ikeda K, Hirayama T, Takazawa T, Kawabe K, Iwasaki Y. Relationships between disease progression and serum levels of lipid, urate, creatinine and ferritin in Japanese patients with amyotrophic lateral sclerosis: a cross-sectional study. Intern Med. 2012;51:1501–8.

    Article  CAS  PubMed  Google Scholar 

  43. Rafiq MK, Lee E, Bradburn M, McDermott CJ, Shaw PJ. Effect of lipid profile on prognosis in the patients with amyotrophic lateral sclerosis: Insights from the olesoxime clinical trial. Amyotroph Lateral Scler Frontotemporal Degener. 2015;16:478–84.

    Article  CAS  PubMed  Google Scholar 

  44. Delaye JB, Patin F, Piver E, Bruno C, Vasse M, Vourc‘h P, et al. Low IDL-B and high LDL-1 subfraction levels in serum of ALS patients. J Neurol Sci. 2017;380:124–7.

    Article  CAS  PubMed  Google Scholar 

  45. Dupuis L, Corcia P, Fergani A, De Gonzalez Aguilar JL, Bonnefont-Rousselot D, Bittar R, et al. Dyslipidemia is a protective factor in amyotrophic lateral sclerosis. Neurology. 2008;70:1004–9.

    Article  CAS  PubMed  Google Scholar 

  46. Chiò A, Calvo A, Ilardi A, Cavallo E, Moglia C, Mutani R, et al. Lower serum lipid levels are related to respiratory impairment in patients with ALS. Neurology. 2009;73:1681–5.

    Article  PubMed  Google Scholar 

  47. Kim S-M, Noh M-Y, Kim H, Cheon S-Y, Lee KM, Lee J, et al. 25-Hydroxycholesterol is involved in the pathogenesis of amyotrophic lateral sclerosis. Oncotarget. 2017;8:11855–67.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Mariosa D, Hammar N, Malmström H, Ingre C, Jungner I, Ye W, et al. Blood biomarkers of carbohydrate, lipid, and apolipoprotein metabolisms and risk of amyotrophic lateral sclerosis: A more than 20-year follow-up of the Swedish AMORIS cohort. Ann Neurol. 2017;81:718–28.

    Article  CAS  PubMed  Google Scholar 

  49. Pardo-Moreno T, Mohamed-Mohamed H, Suleiman-Martos S, Ramos-Rodriguez JJ, Rivas-Dominguez A, Melguizo-Rodríguez L, et al. Amyotrophic lateral sclerosis and serum lipid level association: a systematic review and meta-analytic study. Int J Mol Sci. 2023;24:8675.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Xia K, Klose V, Högel J, Huang T, Zhang L, Dorst J, et al. Lipids and amyotrophic lateral sclerosis: A two-sample Mendelian randomization study. Eur J Neurol. 2023;30:1899–906.

    Article  PubMed  Google Scholar 

  51. van Rheenen W, van der Spek RAA, Bakker MK, van Vugt JJFA, Hop PJ, Zwamborn RAJ, et al. Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology. Nat Genet. 2021;53:1636–48.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Hoofnagle AN, Heinecke JW. Lipoproteomics: using mass spectrometry-based proteomics to explore the assembly, structure, and function of lipoproteins. J Lipid Res. 2009;50:1967–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Glavinovic T, Thanassoulis G, de Graaf J, Couture P, Hegele RA, Sniderman AD. Physiological bases for the superiority of apolipoprotein B over low-density lipoprotein cholesterol and non–high-density lipoprotein cholesterol as a marker of cardiovascular risk. J Am Heart Assoc. 2022;11:e025858.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Kim S, Lee J-W, Lee Y, Song Y, Linton JA. Association between triglyceride-glucose index and low-density lipoprotein particle size in korean obese adults. Lipids Health Dis. 2023;22:94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Godoy-Corchuelo JM, Fernández-Beltrán LC, Ali Z, Gil-Moreno MJ, López-Carbonero JI, Guerrero-Sola A, et al. Lipid metabolic alterations in the ALS–FTD spectrum of disorders. Biomedicines. 2022;10:1105.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Lord J, Jermy B, Green R, Wong A, Xu J, Legido-Quigley C, et al. Mendelian randomization identifies blood metabolites previously linked to midlife cognition as causal candidates in Alzheimer’s disease. Proc Natl Acad Sci U S A. 2021;118:e2009808118.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Huang S-Y, Yang Y-X, Zhang Y-R, Kuo K, Li H-Q, Shen X-N, et al. Investigating causal relations between circulating metabolites and alzheimer’s disease: a Mendelian randomization study. J Alzheimers Dis. 2022;87:463–77.

    Article  CAS  PubMed  Google Scholar 

  58. Ge A, Sun Y, Kiker T, Zhou Y, Ye K. A metabolome-wide Mendelian randomization study prioritizes potential causal circulating metabolites for multiple sclerosis. J Neuroimmunol. 2023;379:578105.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Pham K, Mulugeta A, Lumsden A, Hyppönen E. A multivariable mendelian randomisation study of serum lipids and dementia risk within the UK Biobank. Alzheimer’s Dementia. 2023;19:e066334.

    Article  Google Scholar 

  60. Bhale AS, Venkataraman K. Leveraging knowledge of HDLs major protein ApoA1: Structure, function, mutations, and potential therapeutics. Biomed Pharmacother. 2022;154:113634.

    Article  CAS  PubMed  Google Scholar 

  61. Sniderman AD, Thanassoulis G, Glavinovic T, Navar AM, Pencina M, Catapano A, et al. Apolipoprotein B particles and cardiovascular disease: a narrative review. JAMA Cardiol. 2019;4:1287–95.

    Article  PubMed  PubMed Central  Google Scholar 

  62. European Alzheimer’s & Dementia Biobank Mendelian Randomization (EADB-MR) Collaboration, Luo J, Thomassen JQ, Bellenguez C, Grenier-Boley B, de Rojas I, et al. Genetic associations between modifiable risk factors and alzheimer disease. JAMA Netw Open. 2023;6:e2313734.

    Article  Google Scholar 

  63. Wingo TS, Cutler DJ, Wingo AP, Le N-A, Rabinovici GD, Miller BL, et al. Association of early-onset alzheimer disease with elevated low-density lipoprotein cholesterol levels and rare genetic coding variants of APOB. JAMA Neurol. 2019;76:809–17.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Ramsey RB, Nicholas HJ. Unusual urinary cholesterol metabolites following intracerebral injection of [4–14C]cholesterol into rats: I. The minor 14C-metabolite. Lipids. 1982;17:263–7.

    Article  CAS  PubMed  Google Scholar 

  65. Pashaei S, Mohammadi P, Yarani R, Haghgoo SM, Emami Aleagha MS. Carbohydrate and lipid metabolism in multiple sclerosis: Clinical implications for etiology, pathogenesis, diagnosis, prognosis, and therapy. Arch Biochem Biophys. 2021;712:109030.

    Article  CAS  PubMed  Google Scholar 

  66. Weinstock-Guttman B, Zivadinov R, Mahfooz N, Carl E, Drake A, Schneider J, et al. Serum lipid profiles are associated with disability and MRI outcomes in multiple sclerosis. J Neuroinflammation. 2011;8:127.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Davidson WS, Jonas A, Clayton DF, George JM. Stabilization of alpha-synuclein secondary structure upon binding to synthetic membranes. J Biol Chem. 1998;273:9443–9.

    Article  CAS  PubMed  Google Scholar 

  68. Fais M, Dore A, Galioto M, Galleri G, Crosio C, Iaccarino C. Parkinson’s disease-related genes and lipid alteration. Int J Mol Sci. 2021;22:7630.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Riboldi GM, Di Fonzo AB. GBA, Gaucher disease, and Parkinson’s disease: from genetic to clinic to new therapeutic approaches. Cells. 2019;8:364.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Kluss JH, Mamais A, Cookson MR. LRRK2 links genetic and sporadic Parkinson’s disease. Biochem Soc Trans. 2019;47:651–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Correia Guedes L, Ferreira JJ, Rosa MM, Coelho M, Bonifati V, Sampaio C. Worldwide frequency of G2019S LRRK2 mutation in Parkinson’s disease: a systematic review. Parkinsonism Relat Disord. 2010;16:237–42.

    Article  CAS  PubMed  Google Scholar 

  72. Williams DM, Bandres-Ciga S, Heilbron K, Hinds D, Noyce AJ, 23andMe Research Team, et al. Evaluating lipid-lowering drug targets for Parkinson’s disease prevention with Mendelian randomization. Ann Neurol. 2020;88:1043–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Park KW, Hwang YS, Lee SH, Jo S, Chung SJ. The effect of blood lipids, type 2 diabetes, and body mass index on Parkinson’s Disease: a Korean Mendelian randomization study. J Mov Disord. 2023;16:79–85.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Wang L, Yang Z, Satoshi F, Prasanna X, Yan Z, Vihinen H, et al. Membrane remodeling by FAM92A1 during brain development regulates neuronal morphology, synaptic function, and cognition. Nat Commun. 2024;15:6209.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Shing K, Sapp E, Boudi A, Liu S, Seeley C, Marchionini D, et al. Early whole-body mutant huntingtin lowering averts changes in proteins and lipids important for synapse function and white matter maintenance in the LacQ140 mouse model. Neurobiol Dis. 2023;187:106313.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Kalinichenko LS, Kornhuber J, Sinning S, Haase J, Müller CP. Serotonin signaling through lipid membranes. ACS Chem Neurosci. 2024;15:1298–320.

    Article  CAS  PubMed  Google Scholar 

  77. Mi Y, Qi G, Vitali F, Shang Y, Raikes AC, Wang T, et al. Loss of fatty acid degradation by astrocytic mitochondria triggers neuroinflammation and neurodegeneration. Nat Metab. 2023;5:445–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Parhizkar S, Holtzman DM. APOE mediated neuroinflammation and neurodegeneration in Alzheimer’s Disease. Semin Immunol. 2022;59:101594.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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  1. Zeyu Wang and Zixiao Yin share co-first authorship.

Authors and Affiliations

  1. Department of Gastroenterology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China

    Zeyu Wang, Guangyong Sun & Dong Zhang

  2. Medical Research Center, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China

    Zeyu Wang, Guangyong Sun & Dong Zhang

  3. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

    Zixiao Yin & Jianguo Zhang

  4. Beijing Key Laboratory of Neurostimulation, Beijing, 100070, China

    Zixiao Yin & Jianguo Zhang

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ZW and ZY designed the project, analyzed most of the data, and wrote the manuscript. GS assisted in project design and manuscript revision. JZ and DZ designed the project, supervised the study, and revised the manuscript. All the authors read and approved the final manuscript.

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Wang, Z., Yin, Z., Sun, G. et al. Genetic evidence for the liver-brain axis: lipid metabolism and neurodegenerative disease risk. Lipids Health Dis 24, 41 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-025-02455-3

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Keywords

Lipids in Health and Disease

ISSN: 1476-511X