Circulating Glutamine and Alzheimer's Disease: A Mendelian Randomization Study

Unravelling genetic architecture of circulatory amino acid levels, and their effect on risk of complex disorders

Variations in serum amino acid levels are linked to a multitude of complex disorders. We report the largest genome-wide association study (GWAS) on nine serum amino acids in the UK Biobank participants (117 944, European descent). We identified 34 genomic loci for circulatory levels of alanine, 48 loci for glutamine, 44 loci for glycine, 16 loci for histidine, 11 loci for isoleucine, 19 loci for leucine, 9 loci for phenylalanine, 32 loci for tyrosine and 20 loci for valine. Our gene-based analysis mapped 46-293 genes associated with serum amino acids, including MIP, GLS2, SLC gene family, GCKR, LMO1, CPS1 and COBLL1.The gene-property analysis across 30 tissues highlighted enriched expression of the identified genes in liver tissues for all studied amino acids, except for isoleucine and valine, in muscle tissues for serum alanine and glycine, in adrenal gland tissues for serum isoleucine and leucine, and in pancreatic tissues for serum phenylalanine. Mendelian randomization (MR) phenome-wide association study analysis and subsequent two-sample MR analysis provided evidence that every standard deviation increase in valine is associated with 35% higher risk of type 2 diabetes and elevated levels of serum alanine and branched-chain amino acids with higher levels of total cholesterol, triglyceride and low-density lipoprotein, and lower levels of high-density lipoprotein. In contrast to reports by observational studies, MR analysis did not support a causal association between studied amino acids and coronary artery disease, Alzheimer's disease, breast cancer or prostate cancer. In conclusion, we explored the genetic architecture of serum amino acids and provided evidence supporting a causal role of amino acids in cardiometabolic health.

Causal association and mediating effect of blood biochemical metabolic traits and brain image-derived endophenotypes on Alzheimer's disease

Background

Recent genetic evidence supports that circulating biochemical and metabolic traits (BMTs) play a causal role in Alzheimer's disease (AD), which might be mediated by changes in brain structure. Here, we leveraged publicly available genome-wide association study data to investigate the intrinsic causal relationship between blood BMTs, brain image-derived phenotypes (IDPs) and AD.

Methods

Utilizing the genetic variants associated with 760 blood BMTs and 172 brain IDPs as the exposure and the latest AD summary statistics as the outcome, we analyzed the causal relationship between blood BMTs and brain IDPs and AD by using a two-sample Mendelian randomization (MR) method. Additionally, we used two-step/mediation MR to study the mediating effect of brain IDPs between blood BMTs and AD.

Results

Twenty-five traits for genetic evidence supporting a causal association with AD were identified, including 12 blood BMTs and 13 brain IDPs. For BMTs, glutamine consistently reduced the risk of AD in 3 datasets. For IDPs, specific alterations of cortical thickness (atrophy in frontal pole and insular lobe, and incrassation in superior parietal lobe) and subcortical volume (atrophy in hippocampus and its subgroups, left accumbens and left choroid plexus, and expansion in cerebral white matter) are vulnerable to AD. In the two-step/mediation MR analysis, superior parietal lobe, right hippocampal fissure and left accumbens were identified to play a potential mediating role among three blood BMTs and AD.

Conclusions

The results obtained in our study suggest that 12 circulating BMTs and 13 brain IDPs play a causal role in AD. Importantly, a subset of BMTs exhibit shared genetic architecture and potentially causal relationships with brain structure, which may contribute to the alteration of brain IDPs in AD.

Mendelian Randomization of Blood Metabolites Suggests Circulating Glutamine Protects Against Late-Onset Alzheimer's Disease

Background

Late-onset Alzheimer's disease (LOAD) represents a growing health burden. Previous studies suggest that blood metabolite levels influence risk of LOAD.

Objective

We used a genetics-based study design which may overcome limitations of other epidemiological studies to assess the influence of metabolite levels on LOAD risk.

Methods

We applied Mendelian randomization (MR) to evaluate bi-directional causal effects using summary statistics from the largest genome-wide association studies (GWAS) of 249 blood metabolites (n = 115,082) and GWAS of LOAD (ncase = 21,982, ncontrol = 41,944).

Results

MR analysis of metabolites as exposures revealed a negative association of genetically-predicted glutamine levels with LOAD (Odds Ratio (OR) = 0.83, 95% CI = 0.73, 0.92) that was consistent in multiple sensitivity analyses. We also identified a positive association of genetically-predicted free cholesterol levels in small LDL (OR = 1.79, 95% CI = 1.36, 2.22) on LOAD. Using genetically-predicted LOAD as the exposure, we identified associations with phospholipids to total lipids ratio in large LDL (OR = 0.96, 95% CI = 0.94, 0.98), but not with glutamine, suggesting that the relationship between glutamine and LOAD is unidirectional.

Conclusions

Our findings support previous evidence that higher circulating levels of glutamine may be a target for protection against LOAD.

Development of a diagnostic and risk prediction model for Alzheimer's disease through integration of single-cell and bulk transcriptomic analysis of glutamine metabolism

Background

In this study, we present a novel system for quantifying glutamine metabolism (GM) to enhance the effectiveness of Alzheimer's disease (AD) diagnosis and risk prediction.

Methods

Single-cell RNA sequencing (scRNA-seq) analysis was utilized to comprehensively assess the expression patterns of GM. The WGCNA algorithm was applied to investigate the most significant genes related to GM. Subsequently, three machine learning algorithms (Boruta, LASSO, and SVM-RFE) were employed to identify GM-associated characteristic genes and develop a risk model. Patients were divided into high- and low-risk groups based on this model. Moreover, we explored biological properties, distinct signaling pathways, and immunological characteristics of AD patients at different risk levels. Finally, in vitro and in vivo models of AD were constructed to validate the characteristics of the feature genes.

Results

Both scRNA-seq and bulk transcriptomic analyses revealed increased GM activity in AD patients, specifically in certain cell subsets (pDC, Tem/Effector helper T cells (LTB), and plasma cells). Cells with higher GM scores demonstrated more significant numbers and strengths of interactions with other cell types. The WGCNA algorithm identified 360 genes related to GM, and a risk score was constructed based on nine characteristic genes (ATP13A4, PIK3C2A, CD164, PHF1, CES2, PDGFB, LCOR, TMEM30A, and PLXNA1) identified through multiple machine learning algorithms displayed reliable diagnostic efficacy for AD onset. Nomograms, calibration curves, and decision curve analysis (DCA) based on these characteristic genes provided significant clinical benefits for AD patients. High-risk AD patients exhibited higher levels of immune-related functions and pathways, increased immune cell infiltration, and elevated expressions of immune modulators. RT-qPCR analysis revealed that the majority of the nine characteristic genes were differentially expressed in AD-induced rat neurons. Knocking down PHF1 could protect against neurite loss and alleviate cell injury in AD neurons. In vivo, down-regulation of PHF1 in AD models decreases GM metabolism levels and modulates the immunoinflammatory response in the brain.

Conclusion

This comprehensive identification of gene expression patterns contributes to a deeper understanding of the underlying pathological mechanisms driving AD pathogenesis. Furthermore, the risk model based on the nine-gene signature offers a promising theoretical foundation for developing individualized treatments for AD patients.

Non-invasive biomarkers for mild cognitive impairment and Alzheimer's disease

Alzheimer's disease is the most common type of dementia in the elderly. It is a progressive degenerative disorder that may begin to develop up to 15 years before clinical symptoms appear. The identification of early biomarkers is crucial to enable a prompt diagnosis and to start effective interventions. In this work, we conducted a metabolomic study using proton Nuclear Magnetic Resonance (1H NMR) spectroscopy in serum samples from patients with neuropathologically confirmed Alzheimer's disease (AD, n = 51), mild cognitive impairment (MCI, n = 27), and cognitively healthy controls (HC, n = 50) to search for metabolites that could be used as biomarkers. Patients and controls underwent yearly clinical follow-ups for up to six years. MCI group included samples from three subgroups of subjects with different disease progression rates. The first subgroup included subjects that remained clinically stable at the MCI stage during the period of study (stable MCI, S-MCI, n = 9). The second subgroup accounted for subjects which were diagnosed with MCI at the moment of blood extraction, but progressed to clinical dementia in subsequent years (MCI-to-dementia, MCI-D, n = 14). The last subgroup was composed of subjects that had been diagnosed as dementia for the first time at the moment of sample collection (incipient dementia, Incp-D, n = 4). Partial Least Square Discriminant Analysis (PLS-DA) models were developed. Three models were obtained, one to discriminate between AD and HC samples with high sensitivity (93.75%) and specificity (94.75%), another model to discriminate between AD and MCI samples (100% sensitivity and 82.35% specificity), and a last model to discriminate HC and MCI with lower sensitivity and specificity (67% and 50%). Differences within the MCI group were further studied in an attempt to determine those MCI subjects that could develop AD-type dementia in the future. The relative concentration of metabolites, and metabolic pathways were studied. Alterations in the pathways of alanine, aspartate and glutamate metabolism, pantothenate and CoA biosynthesis, and beta-alanine metabolism, were found when HC and MCI- D patients were compared. In contrast, no pathway was found disturbed in the comparison of S-MCI with HC groups. These results highlight the potential of 1H NMR metabolomics to support the diagnosis of dementia in a less invasive way, and set a starting point for the study of potential biomarkers to identify MCI or HC subjects at risk of developing AD in the future.

The causal role of circulating amino acids on neurodegenerative disorders: A two-sample Mendelian randomization study

Potential associations between the risk of neurodegenerative diseases and circulating levels of amino acids have been implied in both experimental research and observational studies. However, because of the confounding and reverse causality, the findings could be biased. We aimed to determine whether circulating amino acid levels have potential effects on the risk of neurodegenerative diseases through a more robust analysis. So, we performed a total of two MR analyses, a discovery two-sample MR analysis, and a replication test, using summary-level genome-wide association study (GWAS) data, both with circulating levels of amino acids as exposure and risk of neurodegenerative diseases as an outcome. The potential causalities between nine amino acids (Glutamine [Glu], Leucine [Leu], Isoleucine [Ile], Phenylalanine [Phe], Valine [Val], Alanine [Ala], Tyrosine [Tyr], Histidine [His], and Glycine [Gly]) and six neurodegenerative disorders (Alzheimer's disease [AD], Parkinson's disease [PD], Multiple sclerosis [MS], Frontotemporal dementia [FTD], Lewy body dementia [DLB], Amyotrophic lateral sclerosis [ALS]) were explored in this study. According to the discovery MR analysis, 1 SD. increase in circulating levels of Gln was genetically determined to result in a 13% lower risk of AD (IVW ORSD [95% CI] = 0.872 [0.822, 0.926]; FDR = 7.46 × 10-5 ) while PD risk was decreased to 63% per SD. increase of circulating Leu levels (IVW ORSD [95% CI] = 0.628 [0.467, 0.843]; FDR = 0.021). Results from the replication test provide further evidence of the potential association between circulating Gln levels and AD risk (IVW ORSD [95% CI] = 0.094 [0.028, 0.311]; FDR = 9.98 × 10-4 ). Meanwhile, sensitivity analysis demonstrated that the significant relationships revealed by our two-sample MR outcomes were reliable. Our analyses provided robust evidence of causal associations between circulating levels of Gln and AD risk as well as circulating Leu levels and risk of PD. However, the underlying mechanisms remain to be further investigated.

Causal effect of autoimmune liver diseases on cancer: Meta-analyses of cohort studies and Mendelian randomization study

BACKGROUND AND AIMS

Prior studies suggested that patients with autoimmune liver diseases (AiLDs) had an increased risk of cancer, whereas the causal effect remained unclear.

METHODS

Meta-analyses concerning the relationship between AiLD and cancer risk were performed to calculate the pooled relative risk (RR) and corresponding 95% confidence intervals (CIs). Then, the associations with a p value of <.05 were further validated by two-sample Mendelian randomization studies.

RESULTS

A total of 37 cohort studies covering more than 34 558 patients were included, and we observed an increased risk of overall cancers (pooled RR = 3.64, 95% CI: 2.64-5.03, p < .001) and cancer-related death (pooled RR = 2.48, 95% CI: 1.73-3.53, p < .001) for patients with AiLD. Besides, overall and several site-specific cancers risk were found in patients with primary biliary cholangitis (PBC), autoimmune hepatitis (AIH), and primary sclerosing cholangitis (PSC) (p < .05). However, associations between genetically predisposed AIH, PBC, and PSC and the risk of specific cancers did not reach a significant level, except for PBC and gastric cancer (OR = 0.96, 95% CI: 0.93-0.99; p = .02).

CONCLUSIONS

In addition to hepatobiliary cancer, results from the meta-analyses suggest that patients with AiLD might have an increased risk of several extrahepatobiliary cancers. However, the causal role of AiLD in cancer development needs to be further investigated.

Association Between Human Blood Metabolome and the Risk of Alzheimer's Disease

OBJECTIVE

Alzheimer's disease (AD) is the most common degenerative neurological disorder with limited therapeutic options. Therefore, it is particularly important to explore the potential biomarkers implicated in the occurrence and progression of AD prior to clinical testing.

METHODS

We selected 119 unique blood metabolites from 3 metabolome genome-wide association studies (GWASs) with 147,827 European participants. Summary data about AD were obtained from a GWAS meta-analysis with 63,926 European individuals from the International Genomics of Alzheimer's Project. MR analyses were performed to assess the associations of blood metabolites with AD, and a phenome-wide MR analysis was further applied to ascertain the potential on-target side effects of metabolite interventions.

RESULTS

Four metabolites were identified as causal mediators for AD, including epiandrosterone sulfate (odds ratio [OR] per SD increase: 0.60; 95% confidence interval [CI]: 0.51-0.71; P=6.14×10^-9 ), 5alpha-androstan-3beta-17beta-diol disulfate (OR per SD increase: 0.69; 95% CI: 0.57-0.84; P=1.98×10^-4 ), sphingomyelin (OR per SD increase: 2.53; 95% CI: 1.78-3.59; P=2.10×10^-7 ), and glutamine (OR per SD increase: 0.83; 95% CI: 0.77-0.89; P=2.09×10^-6 ). Phenome-wide MR analysis showed that epiandrosterone sulfate, 5alpha-androstan-3beta-17beta-diol disulfate and sphingomyelin mediated the risk of multiple diseases, and glutamine had beneficial effects on the risk of 4 diseases.

INTERPRETATION

Genetically predicted increased epiandrosterone sulfate, 5alpha-androstan-3beta-17beta-diol disulfate and glutamine might be associated with a decreased risk of AD, while sphingomyelin was associated with an increased risk. Side-effect profiles were characterized to help inform drug target prioritization, and glutamine might be a promising target for the prevention and treatment of AD with no predicted detrimental side effects. This article is protected by copyright. All rights reserved.

Molecular details of aluminium-amyloid β peptide interaction by nuclear magnetic resonance

Alzheimer's disease (AD) is a devastating neurodegenerative condition that poses major challenges to human health. Both amyloid β (Aβ) and metal ions such as aluminium are implicated in the development of AD. By the means of NMR, the interactions of Al³⁺ with Aβ_1-28 peptide as well as the Aβ_1-28 analogues were studied, and the key binding sites of Al³⁺ in Aβ determined. NMR data showed Al³⁺ interacts with Aβ_1-28 at the NH and αH of numerous residues by exhibiting upfield shifts. Using Aβ analogues where His6, His13 and His14 were individually replaced by alanine residue(s), including Aβ H6A, Aβ H13A, Aβ H14A, and Aβ H6,13,14A, the results demonstrated that the histidine residues (His6, His13 and His14) and N-terminal Asp1 were involved in the Al³⁺ coordination. These findings provide, for the first time, the details of the molecular interaction between Al³⁺ and Aβ, which points to the potential role of Al³⁺ in Aβ aggregation, hence in AD development.

Restoration and targeting of aberrant neurotransmitters in Parkinson's disease therapeutics

Neurotransmitters are considered as a fundamental regulator in the process of neuronal growth, differentiation and survival. Parkinson's Disease (PD) occurs due to extensive damage of dopamine-producing neurons; this causes dopamine deficits in the midbrain, followed by the alternation of various other neurotransmitters (glutamate, GABA, serotonin, etc.). It has been observed that fluctuation of neurotransmission in the basal ganglia exhibits a great impact on the pathophysiology of PD. Dopamine replacement therapy, such as the use of L-DOPA, can increase the dopamine level, but it majorly ameliorates the motor symptoms and is also associated with long-term complications (for e.g., LID). While the non-dopaminergic system can efficiently target non-motor symptoms, for instance, the noradrenergic system regulates the synthesis of BDNF via the MAPK pathway, which is important in learning and memory. Herein, we briefly discuss the role of different neurotransmitters, implementation of neurotransmitter receptors in PD. We also illustrate the recent advances of neurotransmitter-based drugs, which are currently under in vivo and clinical studies. Reinstating normal neurotransmitter levels has been believed to be advantageous in the treatment of PD. Thus, there is an increasing demand for drugs that can specifically target the neurotransmission system and reinstate the normal levels of neurotransmitters, which might prevent or delay neurodegeneration in PD.

A Nuclear Magnetic Resonance Spectroscopy Method in Characterization of Blood Metabolomics for Alzheimer's Disease

There is currently a crucial need for improved diagnostic techniques and targeted treatment methods for Alzheimer's disease (AD), a disease which impacts millions of elderly individuals each year. Metabolomic analysis has been proposed as a potential methodology to better investigate and understand the progression of this disease. In this report, we present our AD metabolomics results measured with high resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) on human blood plasma samples obtained from AD and non-AD subjects. Our study centers on developments of AD and non-AD metabolomics differentiating models with procedures of quality assurance (QA) and quality control (QC) through pooled samples. Our findings suggest that analysis of blood plasma samples using HRMAS NMR has the potential to differentiate between diseased and healthy subjects, which has important clinical implications for future improvements in AD diagnosis methodologies.

Investigating Causal Relations Between Circulating Metabolites and Alzheimer's Disease: A Mendelian Randomization Study

BACKGROUND

Metabolomics is a promising approach that can be used to understand pathophysiological pathways of Alzheimer's disease (AD). However, the causal relationships between metabolism and AD are poorly understood.

OBJECTIVE

We aimed to investigate the causal association between circulating metabolites and risk of AD through two-sample Mendelian randomization (MR) approach.

METHODS

Genetic associations with 123 circulating metabolic traits were utilized as exposures. Summary statistics data from International Genomics of Alzheimer's Project was used in primary analysis, including 21,982 AD cases and 41,944 controls. Validation was performed using family history of AD data from UK Biobank (27,696 cases of maternal AD, 14,338 cases of paternal AD, and 272,244 controls). We utilized inverse-variance weighted method as primary method.

RESULTS

We found significantly increased risks of developing AD per standard deviation increase in the levels of circulating ApoB (odd ratio[OR] = 3.18; 95% confidence interval[CI]: 1.52-6.66, p = 0.0022), glycoprotein acetyls (OR = 1.21; 95% CI: 1.05-1.39, p = 0.0093), total cholesterol (OR = 2.73; 95% CI: 1.41-5.30, p = 0.0030), and low-density lipoprotein (LDL) cholesterol (OR = 2.34; 95% CI: 1.53-3.57, p = 0.0001). Whereas glutamine (OR = 0.81; 95% CI: 0.71-0.92, p = 0.0011) were significantly associated with lower risk of AD. We also detected causal effects of several different composition of LDL fractions on increased AD risk, which has been verified in validation. However, we found no association between circulating high-density lipoprotein cholesterol and AD.

CONCLUSION

Our findings suggest causal effects of circulating glycoprotein acetyls, ApoB, LDL cholesterol, and serum total cholesterol on higher risk of AD, whereas glutamine showed the protective effect.

Preserved Consciousness in Alzheimer's Disease and Other Dementias: Caregiver Awareness and Communication Strategies

Alzheimer's disease is an insidious onset neurodegenerative syndrome without effective treatment or cure. It is rapidly becoming a global health crisis that is overwhelming healthcare, society, and individuals. The clinical nature of neurocognitive decline creates significant challenges in bidirectional communication between caregivers and persons with Alzheimer's disease (AD) that can negatively impact quality-of-life. This paper sought to understand how and to what extent would awareness training about the levels of consciousness in AD influence the quality-of-life interactions in the caregiver-patient dyad. A literature review of multiple databases was conducted utilizing a transdisciplinary approach. The sum of findings indicates a positive relationship between enhanced caregiver awareness and training, positive interactions, and improved QOL measures among patients and caregivers. A multidirectional relationship was found among healthcare policies, training and education resources, caregivers, and persons with AD. Specifically, the current lack of policy and inadequate training and educational resources has various detrimental effects on patients and caregivers, while improvements in training and education of caregivers yields positive outcomes in communication and QOL. Furthermore, evidence of preserved consciousness in persons with AD was demonstrated from multiple disciplines, including neurobiological, psychological, and biopsychosocial models. The literature further revealed several methods to access the preserved consciousness in persons with AD and related dementias, including sensory, emotional, and cognitive stimulations. The evidence from the literature suggests a reframed approach to our understanding and treatment of persons with AD is not only warranted, but crucial to address the needs of those affected by AD.