Liver-Brain Axis in Sporadic Alzheimer's Disease: Role of Ten Signature Genes in a Mouse model

Causal relationships between Alzheimer's disease and metabolic dysfunction associated with fatty liver disease: insights from bidirectional network Mendelian Randomization analysis

INTRODUCTION/OBJECTIVES

Several observational investigations have observed the possible links between Alzheimer's disease (AD) and metabolic dysfunction associated with fatty liver disease (MAFLD), yet the underlying causal relationships remain undetermined. This study aimed to systemically infer the causal associations between AD and MAFLD by employing a bidirectional network two-sample Mendelian randomization (MR) analysis.

METHODS

Genome-wide significant (P < 5 × 10- 8) genetic variants associated with AD and MAFLD were selected as instrumental variables (IVs) from the consortium of FinnGen, MRC-IEU, UK biobank, and genome-wide association studies (GWAS), respectively. The study sample sizes range from 55,134 to 423,738 for AD and from 218,792 to 778,614 for MAFLD. In the forward analysis, AD was set as the exposure factor, and MAFLD was employed as the disease outcome. Causal relationships between AD and MAFLD were evaluated using inverse-variance weighted (IVW), MR Egger regression, the weighted median, and weighted mode. Additionally, the reverse MR analysis was conducted to infer causality between MAFLD and AD. Sensitivity analyses were performed to assess the robustness of causal estimates.

RESULTS

In the forward MR analysis, the genetically determined family history of AD was associated with a lower risk of MAFLD (mother's history: ORdiscovery=0.08, 95%CI: 0.03, 0.22, P = 7.91 × 10- 7; ORreplicate=0.83, 95%CI: 0.74, 0.94, P = 3.68 × 10- 3; father's history: ORdiscovery=0.01, 95%CI: 0.01, 0.08, P = 5.48 × 10- 5; ORreplicate=0.79, 95%CI: 0.68, 0.93, P = 4.07 × 10- 3; family history: ORdiscovery=0.84, 95%CI: 0.77, 0.91, P = 6.30 × 10- 5; ORreplicate=0.15, 95%CI: 0.05, 0.41, P = 2.51 × 10- 4) in the primary MAFLD cohort. Consistent findings were observed in an independent MAFLD cohort (all P < 0.05). However, the reverse MR analysis suggested that genetic susceptibility to MAFLD had no causal effects on developing AD.

CONCLUSION

Our study demonstrates a causal association between a family history of AD and a lower risk of MAFLD. It suggests that individuals with a history of AD may benefit from tailored metabolic assessments to better understand their risk of MAFLD, and inform the development of preventive strategies targeting high-risk populations.

Causal relationships between neuropsychiatric disorders and nonalcoholic fatty liver disease: A bidirectional Mendelian randomization study

BACKGROUND

Increasing evidences suggest that nonalcoholic fatty liver disease (NAFLD) is associated with neuropsychiatric disorders. Nevertheless, whether there were causal associations between them remained vague. A causal association between neuropsychiatric disorders and NAFLD was investigated in this study.

METHODS

We assessed the published genome-wide association study summary statistics for NAFLD, seven mental disorder-related diseases and six central nervous system dysfunction-related diseases. The causal relationships were first assessed using two-sample and multivariable Mendelian randomization (MR). Then, sensitivity analyses were performed, followed by a reverse MR analysis to determine whether reverse causality is possible. Finally, we performed replication analyses and combined the findings from the above studies.

RESULTS

Our meta-analysis results showed NAFLD significantly increased the risk of anxiety disorders (OR = 1.016, 95% CI = 1.010-1.021, P value < 0.0001). In addition, major depressive disorder was the potential risk factor for NAFLD (OR = 1.233, 95% CI = 1.063-1.430, P value = 0.006). Multivariable MR analysis showed that the causal effect of major depressive disorder on NAFLD remained significant after considering body mass index, but the association disappeared after adjusting for the effect of waist circumference. Furthermore, other neuropsychiatric disorders and NAFLD were not found to be causally related.

CONCLUSIONS

These results implied causal relationships of NAFLD with anxiety disorders and Major Depressive Disorder. This study highlighted the need to recognize and understand the connection between neuropsychiatric disorders and NAFLD to prevent the development of related diseases.

Intravenous MSC-Treatment Improves Impaired Brain Functions in the R6/2 Mouse Model of Huntington's Disease via Recovered Hepatic Pathological Changes

Huntington's disease (HD), a congenital neurodegenerative disorder, extends its pathological damages beyond the nervous system. The systematic manifestation of HD has been extensively described in numerous studies, including dysfunction in peripheral organs and peripheral inflammation. Gut dysbiosis and the gut-liver-brain axis have garnered greater emphasis in neurodegenerative research, and increased plasma levels of pro-inflammatory cytokines have been identified in HD patients and various in vivo models, correlating with disease progression. In the present study, we investigated hepatic pathological markers in the liver of R6/2 mice which convey exon 1 of the human mutant huntingtin gene. Furthermore, we evaluated the impact of intravenously administered Mesenchymal Stromal Cells (MSCs) on the liver enzymes, changes in hepatic inflammatory markers, as well as brain pathology and behavioral deficits in R6/2 mice. Our results revealed altered enzyme expression and increased levels of inflammatory mediators in the liver of R6/2 mice, which were significantly attenuated in the MSC-treated R6/2 mice. Remarkably, neuronal pathology and altered motor activities in the MSC-treated R6/2 mice were significantly ameliorated, despite the absence of MSCs in the postmortem brain. Our data highlight the importance of hepatic pathological changes in HD, providing a potential therapeutic approach. Moreover, the data open new perspectives for the search in blood biomarkers correlating with liver pathology in HD.

Peripheral blood amyloid-β involved in the pathogenesis of Alzheimer's disease via impacting on peripheral innate immune cells

A key pathological factor of Alzheimer's disease (AD), the most prevalent form of age-related dementia in the world, is excessive β-amyloid protein (Aβ) in extracellular aggregation in the brain. And in the peripheral blood, a large amount of Aβ is derived from platelets. So far, the causality between the levels of peripheral blood Aβ and its aggregation in the brain, particularly the role of the peripheral blood Aβ in the pathology of AD, is still unclear. And the relation between the peripheral blood Aβ and tau tangles of brain, another crucial pathologic factor contributing to the pathogenesis of AD, is also ambiguous. More recently, the anti-Aβ monoclonal antibodies are approved for treatment of AD patients through declining the peripheral blood Aβ mechanism of action to enhance plasma and central nervous system (CNS) Aβ clearance, leading to a decrease Aβ burden in brain and improving cognitive function, which clearly indicates that the levels of the peripheral blood Aβ impacted on the Aβ burden in brain and involved in the pathogenesis of AD. In addition, the role of peripheral innate immune cells in AD remains mostly unknown and the results obtained were controversial. In the present review, we summarize recent studies on the roles of peripheral blood Aβ and the peripheral innate immune cells in the pathogenesis of AD. Finally, based on the published data and our own work, we believe that peripheral blood Aβ plays an important role in the development and progression of AD by impacting on the peripheral innate immune cells.

Machine Learning Analysis Reveals Biomarkers for the Detection of Neurological Diseases

It is critical to identify biomarkers for neurological diseases (NLDs) to accelerate drug discovery for effective treatment of patients of diseases that currently lack such treatments. In this work, we retrieved genotyping and clinical data from 1,223 UK Biobank participants to identify genetic and clinical biomarkers for NLDs, including Alzheimer's disease (AD), Parkinson's disease (PD), motor neuron disease (MND), and myasthenia gravis (MG). Using a machine learning modeling approach with Monte Carlo randomization, we identified a panel of informative diagnostic biomarkers for predicting AD, PD, MND, and MG, including classical liver disease markers such as alanine aminotransferase, alkaline phosphatase, and bilirubin. A multinomial model trained on accessible clinical markers could correctly predict an NLD diagnosis with an accuracy of 88.3%. We also explored genetic biomarkers. In a genome-wide association study of AD, PD, MND, and MG patients, we identified single nucleotide polymorphisms (SNPs) implicated in several craniofacial disorders such as apnoea and branchiootic syndrome. We found evidence for shared genetic risk loci among NLDs, including SNPs in cancer-related genes and SNPs known to be associated with non-brain cancers such as Wilms tumor, leukemia, and colon cancer. This indicates overlapping genetic characterizations among NLDs which challenges current clinical definitions of the neurological disorders. Taken together, this work demonstrates the value of data-driven approaches to identify novel biomarkers in the absence of any known or promising biomarkers.