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

Causal role of blood metabolites in HER-positive and HER-negative breast cancer: a Mendelian randomization (MR) study

BACKGROUND

Previous studies provide evidence that in vivo metabolites are associated with breast cancer (BC). However, the causal relationship between blood metabolites and BC remains unclear.

METHOD

Comprehensive two-sample Mendelian randomization analysis was conducted to determine the causal association between 1400 publicly available genetic data on metabolic factors and human epidermal growth factor receptor positive (HER+) BC or HER- BC in this study.

RESULT

Epiandrosterone sulfate levels (OR = 1.07, 95% CI = 1.02 ~ 1.10, p = 0.0013), 5alpha-androstan-3beta,17beta-diol monosulfate (2) levels (OR = 1.07, 95% CI = 1.03 ~ 1.12, p = 0.0012), glycohyocholate levels (OR = 0.85, 95% CI = 0.77 ~ 0.93, p = 0.0007) and etiocholanolone glucuronide levels (OR = 1.12, 95% CI = 1.05 ~ 1.20, p = 0.0013) were causally correlated with HER+ BC. 5 metabolites were causally correlated with HER- BC: Vanillic acid glycine levels (OR = 1.14, 95% CI = 1.06 ~ 1.22, p = 0.0003), Thyroxine levels (OR = 1.26, 95% CI = 1.11 ~ 1.44, p = 0.0004), 1-palmitoyl-2-linoleoyl-GPI (16:0/18:2) levels (OR = 0.86, 95% CI = 0.79 ~ 0.94, p = 0.0010), N-acetylphenylalanine levels (OR = 1.12, 95% CI = 1.05 ~ 1.19, p = 0.0007) and Glucose-to-mannose ratio (OR = 1.15, 95% CI = 1.06 ~ 1.24, p = 0.0008). Two common causally related metabolites were identified: Gamma-glutamyl glutamate and X-12849 levels.

CONCLUSIONS

Our study has respectively demonstrated the connection between blood metabolites and HER+ or HER- BC by genetic means, thereby offering opportunities for therapeutic targets.

Causal associations between systemic lupus erythematosus and primary biliary cholangitis: A bidirectional Mendelian randomization study

Objectives

The association between systemic lupus erythematosus (SLE) and primary biliary cholangitis (PBC) has been increasingly recognized. However, the existence of causal connections between SLE and PBC has yet to be established. In this study, we aimed to investigate the bidirectional causation between SLE and PBC utilizing Mendelian randomization (MR) analysis.

Methods

We acquired summary data from Genome-wide association studies (GWAS) for SLE and PBC from the IEU Open GWAS and FinnGen database. The inverse variance weighted (IVW) was employed as the key method to ascertain the causality between SLE and PBC. Subsequently, a range of sensitivity analyses were applied. We also performed a fixed-effects model meta-analysis to combine the MR results from different databases. Moreover, multivariable MR were conducted to clarify the roles of potential confounding factors.

Results

Our univariable MR investigation provided compelling evidence supporting a causal relationship between SLE and PBC in both directions. Specifically, the IVW method demonstrated a strong casual effect of SLE on PBC (odds ratio (OR) = 1.17, 95 % confidence interval (CI) = 1.09-1.25, p < 0.001). In addition, the results of reverse MR analysis revealed that genetically predicted PBC was associated with an increased risk of SLE (OR = 1.39, 95 % CI = 1.32-1.45, p < 0.001). The sensitivity analyses indicated the absence of horizontal pleiotropy and heterogeneity. Furthermore, the causality between SLE and PBC remained significant even after adjusting for common risk factors in the multivariable MR analysis.

Conclusions

Our study provides statistical evidence of a potential causal relationship between SLE and PBC, but further research is needed to the explore of the underlying mechanisms of these disorders.

Investigation of the causal relationship between breast cancer and thyroid cancer: a set of two-sample bidirectional Mendelian randomization study

PURPOSE

A potential association between breast (BC) and thyroid cancer (TC) has been observed. We investigated if the relationship between BC and TC is causal using bidirectional Mendelian randomization (MR) in Asian and European populations.

METHODS

BC-linked single nucleotide polymorphisms (SNPs) were acquired from a genome-wide association study (GWAS) conducted by the Breast Cancer Association Consortium and Biobank Japan. The most recent TC GWAS data were obtained from the FinnGen Project and National Biobank of Korea. We assessed the potential causal relationship between BC and TC using various MR methods, including inverse-variance-weighting (IVW). Sensitivity, heterogeneity, and pleiotropic tests were performed to assess reliability.

RESULTS

We found a bidirectional causal association between BC and TC within Europeans (IVW, TC on BC: odds ratio [OR] 1.090, 95% confidence interval [CI]: 1.012-1.173, P = 0.023; BC on TC: OR 1.265, 95% CI: 1.158-1.381, P < 0.001). A one-way causal relationship between BC susceptibility and TC risk was found in Asians (IVW BC on TC: OR 2.274, 95% CI: 2.089-2.475, P < 0.001). Subsequently, we identified a noteworthy bidirectional causal relationship between estrogen receptor (ER)-positive BC and TC (IVW, TC on ER-positive BC: OR 1.104, 95% CI: 1.001-1.212, P = 0.038; ER-positive BC on TC: OR 1.223, 95%CI: 1.072-1.395, P = 0.003), but not ER-negative BC and TC in Europeans.

CONCLUSION

We revealed a reciprocal causal association between ER-positive BC and TC. These findings establish a theoretical framework for the simultaneous surveillance and treatment of BC and TC.

Causality of metabolites and metabolic pathways on cholestatic liver diseases: a Mendelian randomization study

Background and Aims

Blood metabolite abnormalities have revealed an association with cholestatic liver diseases (CLDs), while the underlying metabolic mechanisms have remained sluggish yet. Accordingly, the present evaluation aims to investigate the causal relationship between blood metabolites and the risk of two major CLDs, including primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC).

Methods

Univariable and multivariable Mendelian randomization (MR) approaches were employed to uncover potential causal associations between blood metabolites and 2 CLDs, including PBS and PSC, through extracting instrumental variables (IVs) for metabolites from genome-wide association studies (GWAS) conducted on European individuals. The GWAS summary data of PBC or PSC were sourced from two distinct datasets. The initial analysis employed inverse variance weighted (IVW) and an array of sensitivity analyses, followed by replication and meta-analysis utilizing FinnGen consortium data. Finally, a multivariable MR analysis was carried out to ascertain the independent effects of each metabolite. Furthermore, the web-based tool MetaboAnalyst 5.0 was used to perform metabolic pathway examination.

Results

A genetic causality between 15 metabolites and CLDs was recognized after preliminary analysis and false discovery rate (FDR) correction. Subsequently, 9 metabolites consistently represented an association through replication and meta-analysis. Additionally, the independent causal effects of 7 metabolites were corroborated by multivariable MR analysis. Specifically, the metabolites isovalerylcarnitine (odds ratio [OR] = 3.146, 95% confidence intervals [CI]: 1.471-6.726, p = 0.003), valine (OR = 192.44, 95%CI: 4.949-7483.27, p = 0.005), and mannose (OR = 0.184, 95%CI: 0.068-0.499, p < 0.001) were found to have a causal relationship with the occurrence of PBC. Furthermore, erythrose (OR = 5.504, 95%CI: 1.801-16.821, p = 0.003), 1-stearoylglycerophosphocholine (OR = 6.753, 95%CI: 2.621-17.399, p = 7.64 × 10-5), X-11847 (OR = 0.478, 95%CI: 0.352-0.650, p = 2.28 × 10-6), and X-12405 (OR = 3.765, 95%CI: 1.771-8.005, p = 5.71 × 10-4) were independently associated with the occurrence of PSC. Furthermore, the analysis of metabolic pathways identified seven significant pathways in two CLDs.

Conclusion

The findings of the present study have unveiled robust causal relationships between 7 metabolites and 2 CLDs, thereby providing novel insights into the metabolic mechanisms and therapeutic strategies for these disorders.

New evidence: Metformin unsuitable as routine adjuvant for breast cancer: a drug-target mendelian randomization analysis

PURPOSE

The potential efficacy of metformin in breast cancer (BC) has been hotly discussed but never conclusive. This genetics-based study aimed to evaluate the relationships between metformin targets and BC risk.

METHODS

Metformin targets from DrugBank and genome-wide association study (GWAS) data from IEU OpenGWAS and FinnGen were used to investigate the breast cancer (BC)-metformin causal link with various Mendelian Randomization (MR) methods (e.g., inverse-variance-weighting). The genetic association between type 2 diabetes (T2D) and the drug target of metformin was also analyzed as a positive control. Sensitivity and pleiotropic tests ensured reliability.

RESULTS

The primary targets of metformin are PRKAB1, ETFDH and GPD1L. We found a causal association between PRKAB1 and T2D (odds ratio [OR] 0.959, P = 0.002), but no causal relationship was observed between metformin targets and overall BC risk (PRKAB1: OR 0.990, P = 0.530; ETFDH: OR 0.986, P = 0.592; GPD1L: OR 1.002, P = 0.806). A noteworthy causal relationship was observed between ETFDH and estrogen receptor (ER)-positive BC (OR 0.867, P = 0.018), and between GPD1L and human epidermal growth factor receptor 2 (HER2)-negative BC (OR 0.966, P = 0.040). Other group analyses did not yield positive results.

CONCLUSION

The star target of metformin, PRKAB1, does not exhibit a substantial causal association with the risk of BC. Conversely, metformin, acting as an inhibitor of ETFDH and GPD1L, may potentially elevate the likelihood of developing ER-positive BC and HER2-negative BC. Consequently, it is not advisable to employ metformin as a standard supplementary therapy for BC patients without T2D.

Association between human blood metabolome and the risk of gastrointestinal tumors

BACKGROUND

The prevalence of gastrointestinal tumors continues to be significant. To uncover promising therapeutic targets for these tumors, we rigorously executed a Mendelian randomization (MR) study to comprehensively screen the blood metabolomes for potential causal mediators of five frequently encountered gastrointestinal tumors (Liver Cancer, Colorectal Cancer, Esophageal Cancer, Gastric Cancer and Pancreatic Cancer).

METHODS

We selected a comprehensive set of 137 distinct blood metabolites derived from three large-scale genome-wide association studies (GWASs) involving a total of 147827 participants of European ancestry. The gastrointestinal tumors-related data were obtained from a GWAS conducted within the Finnish study. Through meticulous MR analyses, we thoroughly assessed the associations between blood metabolites and gastrointestinal tumors. Additionally, a phenome-wide MR (Phe-MR) analysis was employed to investigate the potential on-target side effects of metabolite interventions.

RESULTS

We have identified 1 blood metabolites, namely isovalerylcarnitine (ORlog10: 1.01; 95%CI, 1.01-1.02; P = 1.81×10-7), as the potential causal mediators for liver cancer. However, no potential pathogenic mediators were detected for the other four tumors.

CONCLUSIONS

The current systematic MR analysis elucidated the potential role of isovalerylcarnitine as a causal mediator in the development of liver cancer. Leveraging the power of Phe-MR study facilitated the identification of potential adverse effects associated with drug targets for liver cancer prevention. Considering the weighing of pros and cons, isovalerylcarnitine emerges as a promising candidate for targeted drug interventions in the realm of liver cancer prevention.

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.

Association of genetically predicted 486 blood metabolites on the risk of Alzheimer's disease: a Mendelian randomization study

Background

Studies have reported that metabolic disturbance exhibits in patients with Alzheimer's disease (AD). Still, the presence of definitive evidence concerning the genetic effect of metabolites on AD risk remains insufficient. A systematic exploration of the genetic association between blood metabolites and AD would contribute to the identification of new targets for AD screening and prevention.

Methods

We conducted an exploratory two-sample Mendelian randomization (MR) study aiming to preliminarily identify the potential metabolites involved in AD development. A genome-wide association study (GWAS) involving 7,824 participants provided information on 486 human blood metabolites. Outcome information was obtained from a large-scale GWAS meta-analysis of AD, encompassing 21,982 cases and 41,944 controls of Europeans. The primary two-sample MR analysis utilized the inverse variance weighted (IVW) model while supplementary analyses used Weighted median (WM), MR Egger, Simple mode, and Weighted mode, followed by sensitivity analyses such as the heterogeneity test, horizontal pleiotropy test, and leave-one-out analysis. For the further identification of metabolites, replication and meta-analysis with FinnGen data, steiger test, linkage disequilibrium score regression, confounding analysis, and were conducted for further evaluation. Multivariable MR was performed to assess the direct effect of metabolites on AD. Besides, an extra replication analysis with EADB data was conducted for final evaluation of the most promising findings.

Results

After rigorous genetic variant selection, IVW, complementary analysis, sensitivity analysis, replication and meta-analysis with the FinnGen data, five metabolites (epiandrosterone sulfate, X-12680, pyruvate, docosapentaenoate, and 1-stearoylglycerophosphocholine) were identified as being genetically associated with AD. MVMR analysis disclosed that genetically predicted these four known metabolites can directly influence AD independently of other metabolites. Only epiandrosterone sulfate and X-12680 remained suggestive significant associations with AD after replication analysis with the EADB data.

Conclusion

By integrating genomics with metabonomics, this study furnishes evidence substantiating the genetic association of epiandrosterone sulfate and X-12680 with AD. These findings hold significance for the screening, prevention, and treatment strategies for AD.

Identification of blood metabolites associated with risk of Alzheimer's disease by integrating genomics and metabolomics data

Specific metabolites have been reported to be potentially associated with Alzheimer's disease (AD) risk. However, the comprehensive understanding of roles of metabolite biomarkers in AD etiology remains elusive. We performed a large AD metabolome-wide association study (MWAS) by developing blood metabolite genetic prediction models. We evaluated associations between genetically predicted levels of metabolites and AD risk in 39,106 clinically diagnosed AD cases, 46,828 proxy AD and related dementia (proxy-ADD) cases, and 401,577 controls. We further conducted analyses to determine microbiome features associated with the detected metabolites and characterize associations between predicted microbiome feature levels and AD risk. We identified fourteen metabolites showing an association with AD risk. Five microbiome features were further identified to be potentially related to associations of five of the metabolites. Our study provides new insights into the etiology of AD that involves blood metabolites and gut microbiome, which warrants further investigation.

A review of disease risk prediction methods and applications in the omics era

Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.

The bidirectional association between frailty index and cardiovascular disease: A Mendelian randomization study

BACKGROUND AND AIM

Observational studies have suggested a relationship between frailty and cardiovascular disease (CVD), but the causality is still uncertain. We used bidirectional Mendelian randomization (MR) design to investigate the potential causal associations between frailty and four main CVDs, including hypertension, myocardial infarction (MI), heart failure (HF), and atrial fibrillation (AF).

METHODS AND RESULTS

Independent single-nucleotide polymorphisms for frailty index (FI) and CVDs (hypertension, MI, HF, and AF) were selected as genetic instruments based on European-descent genome-wide association studies (GWASs). Summary-level data for outcomes on FI (n = 175,226), hypertension (n = 463,010), MI (n = 171,875), HF (n = 977323), and AF (n = 1,030,836) was derived from five large-scale GWASs of European ancestry. We used the inverse-variance weighted (IVW) method to examine the bidirectional associations between FI and CVDs in the main analyses. In the IVW MR analyses, genetically determined high FI was significantly associated with increased risks of hypertension (odds ratio [OR] per 1-SD increase: 1.07 [95 % confidence interval, 1.05-1.08]), MI (OR per 1-SD increase: 1.74 [1.21-2.51]), HF (OR per 1-SD increase: 1.28 [1.10-1.48]), and AF (OR per 1-SD increase: 1.20 [1.08-1.33]). In addition, genetically determined hypertension (beta: 1.406 [1.225-1.587]), MI (beta: 0.045 [0.023-0.067]), HF (beta: 0.105 [0.066-0.143]) and AF (beta: 0.021 [0.012-0.031]) were significantly associated with high FI. These findings were robustly supported by a series of sensitivity analyses with different MR models.

CONCLUSIONS

We found potential bidirectional causal associations between elevated FI and increased risks of CVD, suggesting mutual risk factors between frailty and CVD.

The Relationship of Circulating Choline and Choline-Related Metabolite Levels with Health Outcomes: A Scoping Review of Genome-Wide Association Studies and Mendelian Randomization Studies

Choline is essential for proper liver, muscle, brain, lipid metabolism, cellular membrane composition, and repair. Understanding genetic determinants of circulating choline metabolites can help identify new determinants of choline metabolism, requirements, and their link to disease endpoints. We conducted a scoping review to identify studies assessing the association of genetic polymorphisms on circulating choline and choline-related metabolite concentrations and subsequent associations with health outcomes. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement scoping review extension. Literature was searched to September 28, 2022, in 4 databases: Embase, MEDLINE, Web of Science, and the Biological Science Index. Studies of any duration in humans were considered. Any genome-wide association study (GWAS) investigating genetic variant associations with circulating choline and/or choline-related metabolites and any Mendelian randomization (MR) study investigating the association of genetically predicted circulating choline and/or choline-related metabolites with any health outcome were considered. Qualitative evidence is presented in summary tables. From 1248 total reviewed articles, 53 were included (GWAS = 27; MR = 26). Forty-two circulating choline-related metabolites were tested in association with genetic variants in GWAS studies, primarily trimethylamine N-oxide, betaine, sphingomyelins, lysophosphatidylcholines, and phosphatidylcholines. MR studies investigated associations between 52 total unique choline metabolites and 66 unique health outcomes. Of these, 47 significant associations were reported between 16 metabolites (primarily choline, lysophosphatidylcholines, phosphatidylcholines, betaine, and sphingomyelins) and 27 health outcomes including cancer, cardiovascular, metabolic, bone, and brain-related outcomes. Some articles reported significant associations between multiple choline types and the same health outcome. Genetically predicted circulating choline and choline-related metabolite concentrations are associated with a wide variety of health outcomes. Further research is needed to assess how genetic variability influences choline metabolism and whether individuals with lower genetically predicted circulating choline and choline-related metabolite concentrations would benefit from a dietary intervention or supplementation.

Causal association between 637 human metabolites and ovarian cancer: a mendelian randomization study

BACKGROUND

Current evidence suggests a significant association between metabolites and ovarian cancer (OC); however, the causal relationship between the two remains unclear. This study employs Mendelian randomization (MR) to investigate the causal effects between different metabolites and OC.

METHODS

In this study, a total of 637 metabolites were selected as the exposure variables from the Genome-wide Association Study (GWAS) database ( http://gwas.mrcieu.ac.uk/datasets/ ). The OC related GWAS dataset (ieu-b-4963) was chosen as the outcome variable. R software and the TwoSampleMR package were utilized for the analysis in this study. MR analysis employed the inverse variance-weighted method (IVW), MR-Egger and weighted median (WM) for regression fitting, taking into consideration potential biases caused by linkage disequilibrium and weak instrument variables. Metabolites that did not pass the tests for heterogeneity and horizontal pleiotropy were considered to have no significant causal effect on the outcome. Steiger's upstream test was used to determine the causal direction between the exposure and outcome variables.

RESULTS

The results from IVW analysis revealed that a total of 31 human metabolites showed a significant causal effect on OC (P < 0.05). Among them, 9 metabolites exhibited consistent and stable causal effects, which were confirmed by Steiger's upstream test (P < 0.05). Among these 9 metabolites, Androsterone sulfate, Propionylcarnitine, 5alpha-androstan-3beta,17beta-diol disulfate, Total lipids in medium VLDL and Concentration of medium VLDL particles demonstrated a significant positive causal effect on OC, indicating that these metabolites promote the occurrence of OC. On the other hand, X-12,093, Octanoylcarnitine, N2,N2-dimethylguanosine, and Cis-4-decenoyl carnitine showed a significant negative causal association with OC, suggesting that these metabolites can inhibit the occurrence of OC.

CONCLUSIONS

The study revealed the complex effect of metabolites on OC through Mendelian randomization. As promising biomarkers, these metabolites are worthy of further clinical validation.

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.

Human blood metabolites and risk of severe COVID-19: A Mendelian randomization study

BACKGROUND

Evidence supports the observational associations of human blood metabolites with the risk of severe COVID-19. However, little is known about the potential pathological mechanisms and the analysis of blood metabolites may offer a better understanding of the underlying biological processes.

METHODS

We applied a two-sample Mendelian randomization (MR) analysis to evaluate relationships between 486 blood metabolites and the risk of severe COVID-19. The inverse-variance weighted (IVW) model was used as the primary two-sample MR analysis method to estimate the causal relationship of the exposure on the outcome. Sensitivity analyses were implemented with Cochran's Q test, MR-Egger intercept test, MR-PRESSO, leave-one-out analysis and the funnel plot.

RESULTS

Four hunderd and eighty six metabolites were included for MR analysis following rigorous genetic variants selection. After MR analyses and sensitivity analysis filtration, we found weak evidence of an association between 3-hydroxybutyrate (odds ratio [OR] = 1.21, 95% CI, 1.07-1.38, p = .0036) and the risk of severe COVID-19. A series of sensitivity analyses have been carried out to confirm the rigidity of the above results.

CONCLUSION

This study suggested a causal relationship between 3-hydroxybutyrate and the severity of COVID-19, thus providing novel insights into biomarkers and pathways for COVID-19 prevention and clinical interventions.

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.

Advances in peripheral blood biomarkers of patients with Alzheimer's disease: Moving closer to personalized therapies

Recently, measurable peripheral biomarkers in the plasma of patients with Alzheimer's disease (AD) have gained considerable clinical interest. Several studies have identified one or more blood signatures that may facilitate the development of novel diagnostic and therapeutic strategies. For instance, changes in peripheral amyloid β42 (Aβ42) levels have been largely investigated in patients with AD and correlated with the progression of the pathology, although with controversial results. In addition, tumor necrosis factor α (TNFα) has been identified as an inflammatory biomarker strongly associated with AD, and several studies have consistently suggested the pharmacological targeting of TNFα to reduce systemic inflammation and prevent neurotoxicity in AD. Moreover, alterations in plasma metabolite levels appear to predict the progression of systemic processes relevant to brain functions. In this study, we analyzed the changes in the levels of Aβ42, TNFα, and plasma metabolites in subjects with AD and compared the results with those in healthy elderly (HE) subjects. Differences in plasma metabolites of patients with AD were analyzed with respect to Aβ42, TNFα, and the Mini-Mental State Examination (MMSE) score, searching for plasma signatures that changed simultaneously. In addition, the phosphorylation levels of the Tyr682 residue of the amyloid precursor protein (APP), which we previously proposed as a biomarker of AD, were measured in five HE and five AD patients, in whom the levels of Aβ42, TNFα, and two plasma lipid metabolites increased simultaneously. Overall, this study highlights the potential of combining different plasma signatures to define specific clinical phenotypes of patient subgroups, thus paving the way for the stratification of patients with AD and development of personalized approaches.

Exploring the bidirectional relationship between pain and mental disorders: a comprehensive Mendelian randomization study

BACKGROUND

The close relationship between pain and mental health problems is well-known, and psychological intervention can provide an effective alternative to medication-based pain relief. However, previous studies on the connection between pain and psychological problems, the findings thus far have been inconclusive, limiting the potential for translating psychological interventions into clinical practice. To complement the gap, this study utilized genetic data and Mendelian randomization (MR) to examine the potential relationship between pain in different parts and common mental disorders.

METHODS

Based on the instrumental variables selected from the Genome-wide association study summary statistics of localized pain and mental disorders, we conducted bidirectional two-sample MR analyses to infer bidirectional causal associations between pain and mental disorders. The inverse-variance weighted MR method and MR-Egger were used as the primary statistical method according to the horizontal pleiotropy and heterogeneity level. We reported the odds ratio to infer the causal effect between pain and mental disorders. F statistic was calculated to measure the statistical efficacy of the analyses.

RESULTS

Insomnia is causally related to the genetic susceptibility of multisite pain including head (OR = 1.09, 95% CI: 1.06-1.12), neck/shoulder (OR = 1.12, 95% CI: 1.07-1.16), back (OR = 1.12, 95% CI: 1.07-1.18) and hip (OR = 1.08, 95% CI: 1.05-1.10). Reversely, headache (OR = 1.14, 95% CI: 1.05-1.24), neck/shoulder pain (OR = 1.95, 95% CI: 1.03-3.68), back pain (OR = 1.40, 95% CI: 1.22-1.60), and hip pain (OR = 2.29, 95% CI: 1.18-4.45) promote the genetic liability of insomnia. Depression is strongly associated with the predisposition of multisite pain including headache (OR = 1.28, 95% CI: 1.08-1.52), neck/shoulder pain (OR = 1.32, 95% CI: 1.16-1.50), back pain (OR = 1.35, 95% CI: 1.10-1.66) and stomach/abdominal pain (OR = 1.14, 95% CI: 1.05-1.25), while headache (OR = 1.06, 95% CI: 1.03-1.08), neck/shoulder (OR = 1.09, 95% CI: 1.01-1.17), back (OR = 1.08, 95% CI: 1.03-1.14), and stomach/abdominal pain (OR = 1.19, 95% CI: 1.11-1.26) are predisposing factors for depression. Additionally, insomnia is associated with the predisposition of facial, stomach/abdominal, and knee pain, anxiety was associated with the predisposition of neck/shoulder and back pain, while the susceptibilities of hip and facial pain are influenced by depression, but these associations were unidirectional.

CONCLUSIONS

Our results enhance the understanding of the complex interplay between pain and mental health and highlight the importance of a holistic approach to pain management that addresses both physical and psychological factors.

Omics-based biomarkers discovery for Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disorders presenting with the pathological hallmarks of amyloid plaques and tau tangles. Over the past few years, great efforts have been made to explore reliable biomarkers of AD. High-throughput omics are a technology driven by multiple levels of unbiased data to detect the complex etiology of AD, and it provides us with new opportunities to better understand the pathophysiology of AD and thereby identify potential biomarkers. Through revealing the interaction networks between different molecular levels, the ultimate goal of multi-omics is to improve the diagnosis and treatment of AD. In this review, based on the current AD pathology and the current status of AD diagnostic biomarkers, we summarize how genomics, transcriptomics, proteomics and metabolomics are all conducing to the discovery of reliable AD biomarkers that could be developed and used in clinical AD management.