A genome-wide association study of the human metabolome in a community-based cohort

Deciphering the causal link between gut microbiota and membranous nephropathy: insights into potential inflammatory mechanisms

BACKGROUND

Membranous nephropathy (MN), a leading cause of adult nephrotic syndrome and renal failure, has been linked to gut microbiota (GM) and their metabolites. However, direct causal relationships and therapeutic implications remain unclear.

METHODS

We utilized a comprehensive GWAS dataset that encompasses GM, metabolites, and MN through two-sample Mendelian randomization (MR) analyses, bidirectional MR evaluations, and detailed sensitivity tests.

RESULTS

We identified strong causal associations between nine specific types of GM, including class Clostridia (OR = 1.816, 95%CI: 1.021-3.236, p = .042), class Melainabacteria (OR = 0.661, 95%CI: 0.439-0.996, p = .048), order Gastranaerophilales (OR = 0.689, 95%CI: 0.480-0.996, p = .044), genus Alistipes (OR = 0.480, 95%CI: 0.223-0.998, p = .049), genus Butyricicoccus (OR = 0.464, 95%CI: 0.216-0.995, p = .048), genus Butyrivibrio (OR = 0.799, 95%CI: 0.639-0.998, p = .048), genus Ruminococcaceae UCG003 (OR = 0.563, 95%CI: 0.362-0.877, p = .011), genus Streptococcus (OR = 0.619, 95%CI: 0.393-0.973, p = .038), and genus Oscillibacter (OR = 1.90, 95%CI: 1.06-3.40, p = .031). Additionally, the metabolite tryptophan also exhibited a significant causal influence on MN (OR = 0.852, 95%CI: 0.754-0.963, p = .010). Sensitivity and reverse MR analyses confirmed the robustness of these findings. Further exploration using gutMGene database suggests that GM may influence MN by affecting the release of inflammatory factors and modulating inflammatory pathways.

CONCLUSION

This study offers a comprehensive understanding of the causal links between GM, their metabolites, and MN, which highlight potential pathways for developing new preventive and therapeutic strategies for this condition.

A novel metabolomic aging score - better than conventional metrics in predicting short-term mortality

INTRODUCTION

Accurate prediction of short-term mortality is crucial for optimizing clinical prognosis and providing treatment decisions. Conventional metrics, including physiological indicators, laboratory indexes and scoring systems, suffer from limitations in comprehensiveness, accuracy, and dynamism. In contrast, the metabolomic aging score, as an emerging biomarker, offers substantial promise in short-term mortality prediction.

AREAS COVERED

By integrating multiple metabolites associated with aging and mortality, the score captures dynamic metabolic shifts, providing a real-time reflection of an individual's health status. This approach enables a more precise assessment of short-term mortality risk across diverse diseases, setting it apart from traditional, disease-specific biomarkers. In addition, the metabolic aging score also shows great application prospects in identifying susceptible populations and providing individualized precision medication. This article discusses the novel role of the metabolomic aging score in mortality prediction, highlighting its superior accuracy compared to conventional metrics.

EXPERT OPINION

This score has broad application prospects in the future and also faces challenges such as large-scale validation and standardization. Furthermore, the integration of artificial intelligence (AI) is poised to enhance the clinical utility of the metabolomic aging score, advancing its potential to transform healthcare practices.

Cross-ancestry analyses of Chinese and European populations reveal insights into the genetic architecture and disease implication of metabolites

Differential susceptibilities to various diseases and corresponding metabolite variations have been documented across diverse ethnic populations, but the genetic determinants of these disparities remain unclear. Here, we performed large-scale genome-wide association studies of 171 directly quantifiable metabolites from a nuclear magnetic resonance-based metabolomics platform in 10,792 Han Chinese individuals. We identified 15 variant-metabolite associations, eight of which were successfully replicated in an independent Chinese population (n = 4,480). By cross-ancestry meta-analysis integrating 213,397 European individuals from the UK Biobank, we identified 228 additional variant-metabolite associations and improved fine-mapping precision. Moreover, two-sample Mendelian randomization analyses revealed evidence that genetically predicted levels of triglycerides in high-density lipoprotein were associated with a higher risk of coronary artery disease and that of glycine with a lower risk of heart failure in both ancestries. These findings enhance our understanding of the shared and specific genetic architecture of metabolites as well as their roles in complex diseases across populations.

Examining the link between 179 lipid species and 7 diseases using genetic predictors

BACKGROUND

Genome-wide association studies of lipid species have identified several loci shared with various diseases, however, the relationship between lipid species and disease risk remains poorly understood. Here we investigated whether the plasma levels of lipid species are causally linked to disease risk.

METHODS

We built genetic predictors of 179 lipid species, measured in 7174 Finnish individuals, by utilising either 11 high-impact genomic loci or genome-wide polygenic scores (PGS). We assessed the impact of the lipid species on seven diseases by performing disease association across FinnGen (n = 500,348), UK Biobank (n = 420,531), and Generation Scotland (n = 20,032). We performed univariable Mendelian randomisation (MR) and multivariable MR (MVMR) analyses to examine whether lipid species impact disease risk independently of standard lipids.

FINDINGS

PGS explained >4% of the variance for 34 lipid species but variants outside the high-impact loci had only a marginal contribution. Variants within the high-impact loci showed association with all seven diseases. MVMR supported a causal role of ApoB in ischaemic heart disease after accounting for lipid species. Phosphatidylethanolamine-increasing LIPC variants seemed to lower age-related macular degeneration risk independently of HDL-cholesterol. MVMR suggested a protective effect of four lipid species containing arachidonic acid on cholelithiasis risk independently of Total Cholesterol.

INTERPRETATION

Our study demonstrates how genetic predictors of lipid species can be utilised to gain insights into disease risk. We report potential links between lipid species and age-related macular degeneration and cholelithiasis risk, which can be explored for their utility in disease risk prediction and therapy.

FUNDING

The funders had no role in the study design, data analyses, interpretation, or writing of this article.

Gut metabolites and functional recovery after ischemic stroke: a genetic perspective

The current study explores the relationship between genetically predicted gut metabolites and functional outcomes following ischemic stroke, utilizing the Mendelian Randomization (MR) framework. Genetic information regarding gut microbiota-derived metabolites was sourced from 2076 participants of European descent participating in the Framingham Heart Study. Data on functional outcomes 90 days post-ischemic stroke were acquired from the Genetics of Ischemic Stroke Functional Outcomes Network (n = 6,021). Genetic proxies for gut microbiota were identified from a large-scale GWAS study by the MiBioGen consortium, encompassing 18,340 samples across 24 distinct cohorts. The inverse variance weighting method served as the primary analytical approach. Host gene-influenced gut microbiota was linked to both favorable and unfavorable functional outcomes post-ischemic stroke, involving nine and two specific microbiomes, respectively. Moreover, genetically predicted metabolites of gut microbiota showed associations with functional outcomes post-ischemic stroke, exhibiting one positive and five negative correlations. Sensitivity analyses employing alternative methods and models, not adjusted for baseline stroke severity, consistently supported these findings. This research provides genetic substantiation of the influence of specific gut microbiota and metabolites on the recovery process following ischemic stroke, suggesting a potential causal relationship. This insight offers valuable perspectives on the trajectory of post-stroke recovery and prognostic development.

Assessing causality between mitochondrial-associated proteins with musculoskeletal diseases: A Mendelian randomization study

Musculoskeletal diseases are the leading cause of disability-adjusted life years. Mitochondria, often referred to as the "powerhouses" of cells, are believed to play a role in regulating cellular metabolism and differentiation, potentially influencing the occurrence and progression of musculoskeletal diseases. However, the exact causal relationships remain to be defined. This study aimed to investigate the causal relationships between mitochondrial biological functions and musculoskeletal diseases (including osteoarthritis (OA), osteoporosis, rheumatoid arthritis (RA), and ankylosing spondylitis through Mendelian randomization (MR) analysis). We systematically summarized data related to mitochondrial functional proteins and musculoskeletal diseases from the IEU OpenGWAS and UK Biobank databases. We used single nucleotide polymorphisms significantly associated with musculoskeletal diseases as instrumental variables. The inverse variance weighting method performed the main MR analysis. We used Mendelian randomized residual sum of pleiotropy and outliers, MR-Egger regression, Cochran Q statistic, Rucker Q statistic, Radial-MR, weighted median, simple mode, weighted mode, and leave-one-out analysis methods as supplementary analyses. First, 14 positive mitochondrial functional proteins were screened out. After Bonferroni correction, COA3 and COX4I2 were found to be causally related to OA and act as protective factors. We identified a causal relationship between SLC25A18 and RA as a risk factor. This study provides genetic support and offers new evidence regarding the roles of COA3, COX4I2, and SLC25A18 in the pathophysiology of OA and RA. This study paves the way for a deeper understanding of the pathological mechanisms of musculoskeletal diseases and provides information for their prevention strategies and treatments.

Merging metabolomics and genomics provides a catalog of genetic factors that influence molecular phenotypes in pigs linking relevant metabolic pathways

BACKGROUND

Metabolomics opens novel avenues to study the basic biological mechanisms underlying complex traits, starting from characterization of metabolites. Metabolites and their levels in a biofluid represent simple molecular phenotypes (metabotypes) that are direct products of enzyme activities and relate to all metabolic pathways, including catabolism and anabolism of nutrients. In this study, we demonstrated the utility of merging metabolomics and genomics in pigs to uncover a large list of genetic factors that influence mammalian metabolism.

RESULTS

We obtained targeted characterization of the plasma metabolome of more than 1300 pigs from two populations of Large White and Duroc pig breeds. The metabolomic profiles of these pigs were used to identify genetically influenced metabolites by estimating the heritability of the level of 188 metabolites. Then, combining breed-specific genome-wide association studies of single metabolites and their ratios and across breed meta-analyses, we identified a total of 97 metabolite quantitative trait loci (mQTL), associated with 126 metabolites. Using these results, we constructed a human-pig comparative catalog of genetic factors influencing the metabolomic profile. Whole genome resequencing data identified several putative causative mutations for these mQTL. Additionally, based on a major mQTL for kynurenine level, we designed a nutrigenetic study feeding piglets that carried different genotypes at the candidate gene kynurenine 3-monooxygenase (KMO) varying levels of tryptophan and demonstrated the effect of this genetic factor on the kynurenine pathway. Furthermore, we used metabolomic profiles of Large White and Duroc pigs to reconstruct metabolic pathways using Gaussian Graphical Models, which included perturbation of the identified mQTL.

CONCLUSIONS

This study has provided the first catalog of genetic factors affecting molecular phenotypes that describe the pig blood metabolome, with links to important metabolic pathways, opening novel avenues to merge genetics and nutrition in this livestock species. The obtained results are relevant for basic and applied biology and to evaluate the pig as a biomedical model. Genetically influenced metabolites can be further exploited in nutrigenetic approaches in pigs. The described molecular phenotypes can be useful to dissect complex traits and design novel feeding, breeding and selection programs in pigs.

Family study of bipolar disorder with comorbid anxiety disorder points to THSD7A with possible role of parent-of-origin effect

Aim

The aim of this study was to provide new insights into the genetics of bipolar disorder (BD) by analyzing BD comorbid with anxiety disorders.

Methods

Structured interviews were conducted with BD patients and their parents. Cases were classified into those with comorbid anxiety spectrum (AS) and those without. The family history of patients with BD with comorbid AS was assessed. Focusing on parent-of-origin effects and genomic imprinting from the results, imprinted genes and tested single nucleotide polymorphisms (SNPs) in the identified genes were investigated for an association with BD by transmission disequilibrium test (TDT) using published whole-exome sequencing data.

Results

The incidence of comorbid AS among all the patients with BD analyzed in this study was 39.6%. Patients with BD whose fathers had AS or mood disorders exhibited a significantly higher rate of AS. Among the known imprinted genes, two were associated with BD: THSD7A and CACNA1C. By pruning SNPs, six variants of the THSD7A exons and four variants of the CACNA1C exons were included in the analysis. Among these, one variant of THSD7A, rs2074603, showed over-transmission from parents to patients with BD. Furthermore, it was nominally significant only for fathers when TDT was performed separately for fathers and mothers.

Conclusion

THSD7A may play a role in BD with parent-of-origin effects. Further research is necessary to explore the mechanisms by which genomic imprinting is associated with BD. Clinical Trial Registration: N/A.

Genome-wide association study reveals shared and distinct genetic architecture of fatty acids and oxylipins in the Hispanic Community Health Study/Study of Latinos

Bioactive fatty acid-derived oxylipin molecules play key roles mediating inflammation and oxidative stress. Circulating levels of fatty acids and oxylipins are influenced by environmental and genetic factors; characterizing the genetic architecture of bioactive lipids could yield new insights into underlying biology. We performed a genome-wide association study (GWAS) of 81 fatty acids and oxylipins in 11,584 Hispanic Community Health Study/Study of Latinos (HCHS/SOL) participants with genetic and lipidomic data measured at study baseline (58.6% female, mean age = 46.1 years (standard deviation 13.8)). Additionally, given the effects of central obesity on inflammation, we examined interactions with waist circumference using two-degree-of-freedom joint tests. Thirty-three of the 81 oxylipins and fatty acids were significantly heritable (heritability range: 0-32.7%). Forty (49.4%) oxylipins and fatty acids had at least one genome-wide significant (p < 6.94E-11) variant resulting in 19 independent genetic loci. Six loci (lead variant minor allele frequency [MAF] range: 0.08-0.50), including desaturase-encoding FADS and OATP1B1 transporter protein-encoding SLCO1B1, exhibited associations with two or more fatty acids and oxylipins. At several of these loci, there was evidence of colocalization of the top variant across fatty acids and oxylipins. The remaining loci were only associated with one oxylipin or fatty acid and included several CYP loci. We also identified an additional rare variant (MAF = 0.002) near CARS2 in two-degree-of-freedom tests. Our analyses revealed shared and distinct genetic architecture underlying fatty acids and oxylipins, providing insights into genetic factors and motivating work to characterize these compounds and elucidate their roles in disease.

Association of maternal gut microbial metabolites with gestational diabetes mellitus: evidence from an original case-control study, meta-analysis, and Mendelian randomization

BACKGROUND

The associations of gut microbial metabolites, such as trimethylamine N-oxide (TMAO), its precursors, and phenylacetylglutamine (PAGln), with the risk of gestational diabetes mellitus (GDM) remain unclear.

METHODS

Serum samples of 201 women with GDM and 201 matched controls were collected and then targeted metabolomics was performed to examine the metabolites of interest. Multivariable conditional logistic regression was applied to investigate the relationship between metabolites and GDM. Meta-analysis was performed to combine our results and four similar articles searched from online databases, and Mendelian randomization (MR) analysis was eventually conducted to explore the causalities.

RESULTS

In the case-control study, after dichotomization and comparing the higher versus the lower group, the adjusted odds ratio and 95% confidence interval of choline and L-carnitine with GDM were 2.124 (1.186-3.803) and 0.293 (0.134-0.638), respectively; but neutral relationships between TMAO, betaine, and PAGln with GDM were observed. The following meta-analysis consistently revealed that L-carnitine was negatively associated with GDM. However, MR analyses showed no evidence of causalities.

CONCLUSIONS

Maternal levels of L-carnitine were related to the risk of GDM in both the original case-control study and meta-analysis. However, we did not observe any genetic evidence to establish a causal relationship between this metabolite and GDM.

Mendelian randomization analysis of the causal relationship between trimethylamine N-oxide and its precursors and Parkinson's disease

Introduction

Previous studies have reported a potential association between trimethylamine N-oxide (TMAO) and Parkinson's disease (PD). The objective of this study was to examine the potential relationship between the levels of circulating TMAO and its precursors and the risk of PD using a two-sample Mendelian randomization (MR) approach.

Material and methods

We aggregated data from three genome-wide association studies (International Parkinson's Disease Genomics Consortium, Parkinson's Research: The Organized Genetics Initiative and GenePD, and FinnGen) to extract single-nucleotide polymorphisms (SNPs) associated with circulating concentrations of TMAO, choline, carnitine, and betaine. These SNPs were employed as instrumental variables in a random-effects model to evaluate the causal relationship between circulating concentrations of TMAO and its precursors and the risk of Parkinson's disease, by estimating odds ratios with accompanying 95% confidence intervals. The primary analysis employed the inverse variance-weighted (IVW) method, which was complemented with MR-Egger regression analysis.

Results

The analysis using the IVW method, which aggregated data from the three databases, did not show any causal relationship between circulating concentrations of TMAO and its precursors, and the risk of PD (p > 0.05). This finding was further confirmed by the results of the MR-Egger analysis. A sensitivity analysis demonstrated that the results were not influenced by any biases, and a heterogeneity test indicated no significant variation among the SNPs.

Conclusions

This study did not identify any conclusive evidence of a causal association between the circulating concentrations of TMAO or its precursors and the risk of PD. Further investigation is warranted to determine whether such an association indeed exists.

Advancements in Mass Spectrometry-Based Targeted Metabolomics and Lipidomics: Implications for Clinical Research

Targeted metabolomics and lipidomics are increasingly utilized in clinical research, providing quantitative and comprehensive assessments of metabolic profiles that underlie physiological and pathological mechanisms. These approaches enable the identification of critical metabolites and metabolic alterations essential for accurate diagnosis and precision treatment. Mass spectrometry, in combination with various separation techniques, offers a highly sensitive and specific platform for implementing targeted metabolomics and lipidomics in clinical settings. Nevertheless, challenges persist in areas such as sample collection, quantification, quality control, and data interpretation. This review summarizes recent advances in targeted metabolomics and lipidomics, emphasizing their applications in clinical research. Advancements, including microsampling, dynamic multiple reaction monitoring, and integration of ion mobility mass spectrometry, are highlighted. Additionally, the review discusses the critical importance of data standardization and harmonization for successful clinical implementation.

Circulating dimethylguanidino valeric acid, dietary factors, and risk of coronary heart disease

AIMS

Circulating dimethylguanidino valeric acid (DMGV) was identified as a novel metabolite related to cardiorespiratory fitness and cardiometabolic abnormalities. Circulating DMGV levels are subjective to dietary modulation; however, studies on its associations with intakes of coronary heart disease (CHD)-related foods/nutrients are limited. We investigated whether plasma DMGV was related to risk of incident CHD. We tested associations of DMGV with CHD-related dietary intakes measured by 7-day dietary records and estimated corresponding disease risk.

METHODS AND RESULTS

This nested case-control study on the incidence of CHD included 1520 women (760 incident cases of fatal CHD and nonfatal myocardial infarction and 760 controls) from the Nurses' Health Study. Separately, plasma DMGV and CHD-related dietary intakes and cardiometabolic abnormalities were assessed in the Women's Lifestyle Validation Study (WLVS; n = 724). Higher plasma DMGV was related to a greater risk of CHD [relative risk (RR) per 1 SD, 1.26 (95% CI 1.13, 1.40); P-for-linearity = 0.006]. Greater intakes of sodium, energy-dense foods, and processed/red meat were related to higher DMGV levels; every 1 SD intake of sodium was associated with β 0.13 (SE 0.05; P = 0.007) for DMGV Z-scores, which corresponded to a RR of 1.031 (1.016, 1.046) for CHD. High DMGV (the top quartile, Q4) showed a significant RR of 1.60 (1.17, 2.18) after adjusting for diet and lifestyle factors; the RR further adjusting for obesity and hypertension was 1.29 (0.93, 1.79) as compared with the lowest quartile. In both cohorts, greater adiposity and adverse cardiometabolic factor status were significantly related to higher DMGV levels.

CONCLUSION

Higher levels of plasma DMGV, a metabolite reflecting unfavourable CHD-related dietary intakes, were associated with an increased risk of CHD. The unfavourable association was attenuated by cardiometabolic risk factor status. Our study underscores the potential importance of plasma DMGV as an early biomarker associated with diet and the long-term risk of CHD among women.

Causal Relationships Between Gut Microbiota, Metabolites, and Diabetic Nephropathy: Insights from a Two-Sample Mendelian Randomization Analysis

Background

 Previous studies have established a correlation between gut microbiota, metabolites, and diabetic nephropathy (DN). However, the inherent limitations of observational studies, including reverse causality and confounding factors, made this relationship uncertain.

Methods

In this study, we compiled summary statistics from a genome-wide association study (GWAS) conducted on gut microbiota, metabolites, and DN. We employed a two-sample Mendelian randomization (MR) approach, utilizing inverse variance weighted (IVW), MR-Egger, weighted median, and weighted mode methods.

Results

 We detected the protective nature of genetically predicted representatives from the family Bacteroidaceae (OR: 0.716, 95% CI: 0.516-0.995, p = 0.046), family Victivallaceae (OR: 0.871, 95% CI: 0.772-0.982, p = 0.026), genus Bacteroides (OR: 0.716, 95% CI: 0.516-0.995, p = 0.046), genus Coprococcus 2 (OR: 0.745, 95% CI: 0.576-0.963, p = 0.025), and genus Lactococcus (OR: 0.851, 95% CI: 0.730-0.992, p = 0.039) against the development of DN. Conversely, we identified a positive correlation between the incidence of DN and entities, such as Phylum Bacteroidetes (OR: 1.427, 95% CI: 1.085-1.875, p = 0.011), class Bacteroidia (OR: 1.304, 95% CI: 1.036-1.641,p = 0.024), order Bacteroidales (OR: 1.304, 95% CI: 1.035-1.641, p = 0.028), genus Catenibacterium (OR: 1.312, 95% CI: 1.079-1.594, p = 0.006), genus Lachnoclostridium (OR: 1.434, 95% CI: 1.129-1.821, p = 0.003), and genus Parasutterella (OR: 1.270, 95% CI: 1.070-1.510, p = 0.006). In our analysis, none of the gut metabolites demonstrated a causal relationship with DN.

Conclusion

 Our results substantiated the potential causal association between specific gut microbiota and DN. Therefore, our study offers novel insight into the mechanisms underlying DN. This finding provides a theoretical foundation for the future development of targeted strategies for the prevention and treatment of DN.

Beyond conventional treatment: ASGR1 Leading the new era of hypercholesterolemia management

Cardiovascular disease (CVD) remains a leading cause of mortality worldwide, with hypercholesterolemia being a major risk factor. Although various lipid-lowering therapies exist, many patients fail to achieve optimal cholesterol control, highlighting the need for novel therapeutic approaches. ASGR1 (asialoglycoprotein receptor 1), predominantly expressed on hepatocytes, has emerged as a key regulator of cholesterol metabolism and low-density lipoprotein (LDL) clearance. This receptor's ability to regulate lipid homeostasis positions it as a promising target for therapeutic intervention in hypercholesterolemia and related cardiovascular diseases. This review critically examines the biological functions and regulatory mechanisms of ASGR1 in cholesterol metabolism, with a focus on its potential as a therapeutic target for hypercholesterolemia and related cardiovascular diseases. By analyzing recent advances in ASGR1 research, this article explores its role in liver-specific pathways, the implications of ASGR1 variants in CVD risk, and the prospects for developing ASGR1-targeted therapies. This review aims to provide a foundation for future research and clinical applications in hypercholesterolemia management.

Molecular landscape of the overlap between Alzheimer's disease and somatic insulin-related diseases

Alzheimer's disease (AD) is a multifactorial disease with both genetic and environmental factors contributing to its etiology. Previous evidence has implicated disturbed insulin signaling as a key mechanism that plays a role in both neurodegenerative diseases such as AD and comorbid somatic diseases such as diabetes mellitus type 2 (DM2). In this study, we analysed available genome-wide association studies (GWASs) of AD and somatic insulin-related diseases and conditions (SID), i.e., DM2, metabolic syndrome and obesity, to identify genes associated with both AD and SID that could increase our insights into their molecular underpinnings. We then performed functional enrichment analyses of these genes. Subsequently, using (additional) GWAS data, we conducted shared genetic etiology analyses between AD and SID, on the one hand, and blood and cerebrospinal fluid (CSF) metabolite levels on the other hand. Further, integrating all these analysis results with elaborate literature searches, we built a molecular landscape of the overlap between AD and SID. From the landscape, multiple functional themes emerged, including insulin signaling, estrogen signaling, synaptic transmission, lipid metabolism and tau signaling. We also found shared genetic etiologies between AD/SID and the blood/CSF levels of multiple metabolites, pointing towards "energy metabolism" as a key metabolic pathway that is affected in both AD and SID. Lastly, the landscape provided leads for putative novel drug targets for AD (including MARK4, TMEM219, FKBP5, NDUFS3 and IL34) that could be further developed into new AD treatments.

Multiomic integration analysis identifies atherogenic metabolites mediating between novel immune genes and cardiovascular risk

BACKGROUND

Understanding genetic-metabolite associations has translational implications for informing cardiovascular risk assessment. Interrogating functional genetic variants enhances our understanding of disease pathogenesis and the development and optimization of targeted interventions.

METHODS

In this study, a total of 187 plasma metabolite levels were profiled in 4974 individuals of European ancestry of the GCAT| Genomes for Life cohort. Results of genetic analyses were meta-analysed with additional datasets, resulting in up to approximately 40,000 European individuals. Results of meta-analyses were integrated with reference gene expression panels from 58 tissues and cell types to identify predicted gene expression associated with metabolite levels. This approach was also performed for cardiovascular outcomes in three independent large European studies (N = 700,000) to identify predicted gene expression additionally associated with cardiovascular risk. Finally, genetically informed mediation analysis was performed to infer causal mediation in the relationship between gene expression, metabolite levels and cardiovascular risk.

RESULTS

A total of 44 genetic loci were associated with 124 metabolites. Lead genetic variants included 11 non-synonymous variants. Predicted expression of 53 fine-mapped genes was associated with 108 metabolite levels; while predicted expression of 6 of these genes was also associated with cardiovascular outcomes, highlighting a new role for regulatory gene HCG27. Additionally, we found that atherogenic metabolite levels mediate the associations between gene expression and cardiovascular risk. Some of these genes showed stronger associations in immune tissues, providing further evidence of the role of immune cells in increasing cardiovascular risk.

CONCLUSIONS

These findings propose new gene targets that could be potential candidates for drug development aimed at lowering the risk of cardiovascular events through the modulation of blood atherogenic metabolite levels.

Effect of Relative Protein Intake on Hypertension and Mediating Role of Physical Fitness and Circulating Fatty Acids: A Mendelian Randomization Study

OBJECTIVE

To investigate the causal effect of protein intake on hypertension and the related mediating pathways.

PATIENTS AND METHODS

Using genome-wide association study summary statistics of European ancestry, we applied univariable and multivariable Mendelian randomization to estimate the bidirectional associations of relative protein intake and related metabolomic signatures with hypertension (FinnGen: Ncase=42,857/Ncontrol=162,837; UK Biobank: Ncase=77,723/Ncontrol=330,366) and blood pressure (International Consortium of Blood Pressure: N=757,601) and two-step Mendelian randomization to assess the mediating roles of 40 cardiometabolic factors therein. Mendelian randomization estimates of hypertension from FinnGen and UK Biobank were meta-analyzed without heterogeneity. We performed the study from May 15, 2023, to September 15, 2023.

RESULTS

Each 1-SD higher relative protein intake was causally associated with 69% (odds ratio, 0.31; 95% CI, 0.11 to 0.89) lower hypertension risk independent of the effects of other macronutrients, and was the only macronutrient associated with 2.21 (95% CI, 0.52 to 3.91) mm Hg lower pulse pressure, in a unidirectional manner. Higher plant protein-related metabolomic signature (glycine) was associated with lower hypertension risk and pulse pressure, whereas higher animal protein-related metabolomic signatures (leucine, isoleucine, valine, and isovalerylcarnitine [only systolic blood pressure]) were associated with higher hypertension risk, pulse pressure, and systolic blood pressure. The effect of relative protein intake on hypertension was causally mediated by frailty index (mediation proportion, 40.28%), monounsaturated fatty acids (13.81%), saturated fatty acids (11.39%), grip strength (5.34%), standing height (3.99%), and sitting height (3.61%).

CONCLUSION

Higher relative protein intake causally reduces the risk of hypertension, partly mediated by physical fitness and circulating fatty acids.

Deciphering the role of FOXP4 in long COVID: exploring genetic associations, evolutionary conservation, and drug identification through bioinformatics analysis

Long COVID (LC) refers to a condition characterized by a variety of lingering symptoms that persist for more than 4 to 12 weeks following the initial acute SARS-CoV-2 infection. Recent research has suggested that the FOXP4 gene could potentially be a significant factor contributing to LC. Owing to that, this study investigates FOXP4's role in LC by analyzing public datasets to understand its evolution and expression in diverse human populations and searching for drugs to reduce LC symptoms. Population genetic analysis of FOXP4 across human populations unmasks distinct genetic diversity patterns and positive selection signatures, suggesting potential population-specific susceptibilities to conditions like LC. Further, we also observed that FOXP4 experiences high expression during LC. To identify potential inhibitors, drug screening analysis identifies synthetic drugs like Glisoxepide, and natural compounds Kapurimycin A3 produced from Streptomyces sp, and Cucurbitacin B from Begonia nantoensis as promising candidates. Overall, our research contributes to understanding how FOXP4 may serve as a therapeutic target for mitigating the impact of LC.

gsQTL: Associating genetic risk variants with gene sets by exploiting their shared variability

To investigate the functional significance of genetic risk loci identified through genome-wide association studies (GWASs), genetic loci are linked to genes based on their capacity to account for variation in gene expression, resulting in expression quantitative trait loci (eQTL). Following this, gene set analyses are commonly used to gain insights into functionality. However, the efficacy of this approach is hampered by small effect sizes and the burden of multiple testing. We propose an alternative approach: instead of examining the cumulative associations of individual genes within a gene set, we consider the collective variation of the entire gene set. We introduce the concept of gene set QTL (gsQTL), and show it to be more adept at identifying links between genetic risk variants and specific gene sets. Notably, gsQTL experiences less susceptibility to inflation or deflation of significant enrichments compared with conventional methods. Furthermore, we demonstrate the broader applicability of shared variability within gene sets. This is evident in scenarios such as the coordinated regulation of genes by a transcription factor or coordinated differential expression.

Mendelian randomization identifies causal associations between GWAS-associated bacteria and their metabolites and rheumatoid arthritis

Background

Accumulating evidence suggests that an imbalance of gut microbiota is commonly observed in patients with rheumatoid arthritis (RA). However, it remains unclear whether gut microbiota dysbiosis is a cause or consequence of RA, and the mechanisms by which gut dysbiosis contributes to RA have not been fully understood. This study aimed to investigate the causal relationship between gut microbiota and metabolites with RA.

Methods

A two-sample Mendelian randomization analysis was performed to estimate the causality of gut microbiota and metabolites on RA. A genome-wide association study (GWAS) of 211 gut microbiota and 217 metabolites was used as the exposure, whereas RA was treated as the outcome. Inverse variance weighted (IVW) was regarded as the primary approach for calculating causal estimates. MR Egger method, Weighted median method, Simple mode method, and weighted mode method were used for sensitive analysis. Metabolic pathway analysis was performed via the web-based Metaconflict 5.0. Additionally, an animal study was undertaken to evaluate the results inferred by Mendelian randomization.

Result

This study indicated that six gut microbiota taxa (RuminococcaceaeUCG013, Erysipelotrichia, Erysipelotrichaceae, Erysipelotrichales, Clostridia, and Veillonellaceae) were estimated to exert a positive impact on RA. Conversely, seven gut microbiota taxa (Oxalobacter, Cyanobacteria, RuminococcaceaeUCG002, LachnospiraceaeUCG010, Christensenellaceae, Oxalobacteraceae, Anaerostipes) were estimated to exert a negative impact on RA. Three metabolites, namely indole-3-propionate (IPA), glycine and sphingomyelin (SM 16:1), were found to be linked to lower RA risk, while five metabolites (argininosuccinate, CE 20_4, TAG 58_8, PC 40_6, and LPC 20_4) were linked to higher RA risk. Additionally, four metabolic pathways were identified by metabolic pathway analysis. The collagen-induced arthritis (CIA) rats exhibited a higher relative abundance of Class_Clostridia and a lower abundance of Genus_Lachnospiraceae (p < 0.05) than the healthy controls.

Conclusion

This study identified causal associations between specific gut microbiota, metabolites, and RA. These findings support the significant role of gut microbiota and metabolites in RA pathogenesis.

AGXT2 Suppresses the Proliferation and Dissemination of Hepatocellular Carcinoma Cells by Modulating Intracellular Lipid Metabolism

Purpose

Alanine glyoxylate aminotransferase (AGXT) family members are crucial in cancer processes, but their role in hepatocellular carcinoma (HCC) metabolism is unclear. This study investigates AGXT2's function in HCC.

Patients and Methods

AGTX2 expression was studied using bioinformatics, real-time reverse transcriptase-polymerase chain reaction (RT-qPCR), Western blot, and Enzyme-linked immunosorbent assay (ELISA). A lentivirus-induced AGTX2 overexpression cell model was analyzed with RNA sequencing (RNA-seq) and liquid chromatography-mass spectrometry (LC-MS). Cholesterol levels were confirmed by Oil Red O staining. AGTX2 effects were evaluated through cell cycle analysis, wound healing, and transwell migration assays.Tumorigenic effects were observed in NOD-SCID IL2Rγnull (NTG) mice in subcutaneous experiments. Protein interaction was examined through co-immunoprecipitation methods.

Results

We observed a significant reduction in AGXT2 mRNA and protein levels in both HCC tumor tissues and serum samples from patients with liver cancer, which was associated with a worse prognosis. The activation of AGXT2 has been shown to effectively decrease cholesterol levels in liver cancer cells, serving as an antagonist in the cholesterol metabolism pathway. An increase in low density lipoprotein receptor (LDLR) mRNA was noted in cells overexpressing AGXT2, accompanied by a decrease in LDLR protein and an elevation in proprotein convertase subtilisin/kexin type 9 (PCSK9) mRNA and protein levels. Molecular docking and co-immunoprecipitation experiments further elucidated the interaction between AGXT2 and LDLR proteins. AGXT2 was observed to suppress the migratory and invasive capabilities of HCC cells, inducing cell cycle arrest in the G2/M phase. AGXT2 activation inhibited subcutaneous liver cancer tumor growth in NTG mice.

Conclusion

AGXT2 was found to lower cholesterol levels in liver cancer cells, possibly through interactions with the LDLR protein and modulation of PCSK9-mediated LDLR degradation. This mechanism may impede cholesterol transport to liver cancer cells, thereby suppressing their growth and metastasis.

A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes

In-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab). Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations. Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays "The Molecular Human". We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data. Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to "The Molecular Human" ( http://comics.metabolomix.com ), providing interactive data exploration and hypotheses generation possibilities.

Causal relationships between gut microbiota, gut metabolites, and diabetic neuropathy: A mendelian randomization study

BACKGROUND

Previous studies have shown a strong correlation between gut microbiota and diabetes and its associated complications. We aimed to evaluate the causal relationships between the gut microbiota, gut metabolites, and diabetic neuropathy.

METHODS

Summary statistics of 211 gut microbiota and 12 gut-related metabolites (β-hydroxybutyric acid, betaine, trimethylamine-N-oxide, carnitine, choline, glutamate, kynurenine, phenylalanine, propionic acid, serotonin, tryptophan, and tyrosine) were obtained from previous genome-wide association studies (GWAS). A two-sample Mendelian randomization (MR) design was used to estimate the effects of gut microbiota and gut metabolites on the risk of diabetic neuropathy based on FinnGen GWAS.

RESULTS

Higher levels of Acidaminococcaceae (OR = 0.62; 95%CI = 0.46 to 0.84; P = 0.002), Peptococcaceae (OR = 0.70; 95%CI = 0.54 to 0.90; P = 0.006), and Eubacterium coprostanoligenes group (OR = 0.68; 95%CI = 0.50 to 0.93; P = 0.016) are genetically determined to provide protection against diabetic neuropathy. Conversely, the presence of Alistipes (OR = 1.65; 95%CI = 1.18 to 2.31; P = 0.003), ChristensenellaceaeR7 group (OR = 1.52; 95%CI = 1.03 to 2.23; P = 0.033), Eggerthella (OR = 1.28; 95%CI = 1.05 to 1.55; P = 0.014), RuminococcaceaeUCG013 (OR = 1.35; 95%CI = 1.01 to 1.82; P = 0.046), and Firmicutes (OR = 1.42; 95%CI = 1.05 to 1.93; P = 0.023) increases the risk of diabetic neuropathy. Moreover, a correlation has been identified between diabetic neuropathy and two gut metabolites: betaine (OR = 0.95; 95%CI = 0.90 to 1.00; P = 0.033) and tyrosine (OR = 1.03; 95%CI = 1.01 to 1.06; P = 0.019). Sensitivity analysis indicated robust results with no sign of heterogeneity or pleiotropy.

CONCLUSION

The present study elucidated the impact of specific gut microbiota and gut metabolites on the susceptibility to diabetic neuropathy. Interventions targeting the improvement of the gut microbiota diversity and composition hold considerable promise as a potential strategy.

Genetic variants affecting mitochondrial function provide further insights for kidney disease

BACKGROUND

Chronic kidney disease (CKD) is a complex disorder that has become a high prevalence global health problem, with diabetes being its predominant pathophysiologic driver. Autosomal genetic variation only explains some of the predisposition to kidney disease. Variations in the mitochondrial genome (mtDNA) and nuclear-encoded mitochondrial genes (NEMG) are implicated in susceptibility to kidney disease and CKD progression, but they have not been thoroughly explored. Our aim was to investigate the association of variation in both mtDNA and NEMG with CKD (and related traits), with a particular focus on diabetes.

METHODS

We used the UK Biobank (UKB) and UK-ROI, an independent collection of individuals with type 1 diabetes mellitus (T1DM) patients.

RESULTS

Fourteen mitochondrial variants were associated with estimated glomerular filtration rate (eGFR) in UKB. Mitochondrial variants and haplogroups U, H and J were associated with eGFR and serum variables. Mitochondrial haplogroup H was associated with all the serum variables regardless of the presence of diabetes. Mitochondrial haplogroup X was associated with end-stage kidney disease (ESKD) in UKB. We confirmed the influence of several known NEMG on kidney disease and function and found novel associations for SLC39A13, CFL1, ACP2 or ATP5G1 with serum variables and kidney damage, and for SLC4A1, NUP210 and MYH14 with ESKD. The G allele of TBC1D32-rs113987180 was associated with higher risk of ESKD in patients with diabetes (OR:9.879; CI95%:4.440-21.980; P = 2.0E-08). In UK-ROI, AGXT2-rs71615838 and SURF1-rs183853102 were associated with diabetic nephropathies, and TFB1M-rs869120 with eGFR.

CONCLUSIONS

We identified novel variants both in mtDNA and NEMG which may explain some of the missing heritability for CKD and kidney phenotypes. We confirmed the role of MT-ND5 and mitochondrial haplogroup H on renal disease (serum variables), and identified the MT-ND5-rs41535848G variant, along with mitochondrial haplogroup X, associated with higher risk of ESKD. Despite most of the associations were independent of diabetes, we also showed potential roles for NEMG in T1DM.

Unraveling interindividual variation of trimethylamine N-oxide and its precursors at the population level

Trimethylamine N-oxide (TMAO) is a circulating microbiome-derived metabolite implicated in the development of atherosclerosis and cardiovascular disease (CVD). We investigated whether plasma levels of TMAO, its precursors (betaine, carnitine, deoxycarnitine, choline), and TMAO-to-precursor ratios are associated with clinical outcomes, including CVD and mortality. This was followed by an in-depth analysis of their genetic, gut microbial, and dietary determinants. The analyses were conducted in five Dutch prospective cohort studies including 7834 individuals. To further investigate association results, Mendelian Randomization (MR) was also explored. We found only plasma choline levels (hazard ratio [HR] 1.17, [95% CI 1.07; 1.28]) and not TMAO to be associated with CVD risk. Our association analyses uncovered 10 genome-wide significant loci, including novel genomic regions for betaine (6p21.1, 6q25.3), choline (2q34, 5q31.1), and deoxycarnitine (10q21.2, 11p14.2) comprising several metabolic gene associations, for example, CPS1 or PEMT. Furthermore, our analyses uncovered 68 gut microbiota associations, mainly related to TMAO-to-precursors ratios and the Ruminococcaceae family, and 16 associations of food groups and metabolites including fish-TMAO, meat-carnitine, and plant-based food-betaine associations. No significant association was identified by the MR approach. Our analyses provide novel insights into the TMAO pathway, its determinants, and pathophysiological impact on the general population.

Genome-wide association study reveals shared and distinct genetic architecture underlying fatty acid and bioactive oxylipin metabolites in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

Bioactive fatty acid-derived oxylipin molecules play key roles in mediating inflammation and oxidative stress, which underlie many chronic diseases. Circulating levels of fatty acids and oxylipins are influenced by both environmental and genetic factors; characterizing the genetic architecture of bioactive lipids could yield new insights into underlying biological pathways. Thus, we performed a genome wide association study (GWAS) of n=81 fatty acids and oxylipins in n=11,584 Hispanic Community Health Study/Study of Latinos (HCHS/SOL) participants with genetic and lipidomic data measured at study baseline (58.6% female, mean age = 46.1 years, standard deviation = 13.8 years). Additionally, given the effects of central obesity on inflammation, we examined interactions with waist circumference using two-degree-of-freedom joint tests. Heritability estimates ranged from 0% to 47.9%, and 48 of the 81oxylipins and fatty acids were significantly heritable. Moreover, 40 (49.4%) of the 81 oxylipins and fatty acids had at least one genome-wide significant (p< 6.94E-11) variant resulting in 19 independent genetic loci involved in fatty acid and oxylipin synthesis, as well as downstream pathways. Four loci (lead variant minor allele frequency [MAF] range: 0.08-0.50), including the desaturase-encoding FADS and the OATP1B1 transporter protein-encoding SLCO1B1, exhibited associations with four or more fatty acids and oxylipins. The majority of the 15 remaining loci (87.5%) (lead variant MAF range = 0.03-0.45, mean = 0.23) were only associated with one oxylipin or fatty acid, demonstrating evidence of distinct genetic effects. Finally, while most loci identified in two-degree-of-freedom tests were previously identified in our main effects analyses, we also identified an additional rare variant (MAF = 0.002) near CARS2, a locus previously implicated in inflammation. Our analyses revealed shared and distinct genetic architecture underlying fatty acids and oxylipins, providing insights into genetic factors and motivating future multi-omics work to characterize these compounds and elucidate their roles in disease pathways.

Genotype-based "virtual" metabolomics in a clinical biobank identifies novel metabolite-disease associations

Introduction: Circulating metabolites act as biomarkers of dysregulated metabolism and may inform disease pathophysiology. A portion of the inter-individual variability in circulating metabolites is influenced by common genetic variation. We evaluated whether a genetics-based "virtual" metabolomics approach can identify novel metabolite-disease associations. Methods: We examined the association between polygenic scores for 724 metabolites with 1,247 clinical phenotypes in the BioVU DNA biobank, comprising 57,735 European ancestry and 15,754 African ancestry participants. We applied Mendelian randomization (MR) to probe significant relationships and validated significant MR associations using independent GWAS of candidate phenotypes. Results and Discussion: We found significant associations between 336 metabolites and 168 phenotypes in European ancestry and 107 metabolites and 56 phenotypes in African ancestry. Of these metabolite-disease pairs, MR analyses confirmed associations between 73 metabolites and 53 phenotypes in European ancestry. Of 22 metabolitephenotype pairs evaluated for replication in independent GWAS, 16 were significant (false discovery rate p < 0.05). These included associations between bilirubin and X-21796 with cholelithiasis, phosphatidylcholine (16:0/22:5n3,18:1/20:4) and arachidonate with inflammatory bowel disease and Crohn's disease, and campesterol with coronary artery disease and myocardial infarction. These associations may represent biomarkers or potentially targetable mediators of disease risk.

Causal Relationship between Mitochondrial-Associated Proteins and Sepsis in ICU Patients: A Mendelian Randomization Study

BACKGROUND

The alarming mortality rate of sepsis in ICUs has garnered significant attention. The precise etiology remains elusive. Mitochondria, often referred to as the cellular powerhouses, have been postulated to have a dysfunctional role, correlating with the onset and progression of sepsis. However, the exact causal relationship remains to be defined.

METHOD

Employing the Mendelian randomization approach, this study systematically analyzed data from the IEUOpenGWAS and UKbiobank databases concerning mitochondrial function-related proteins and their association with sepsis, aiming to delineate the causal relationship between the two.

RESULTS

The findings underscored a statistically significant association of GrpE1 with sepsis, registering a P value of 0.005 and an OR of 0.499 (95% CI: 0.307-0.810). Likewise, HTRA2, ISCU, and CUP3 each manifested significant associations with sepsis, yielding OR values of 0.585, 0.637, and 0.634, respectively. These results suggest potential implications of the aforementioned proteins in the pathogenesis of sepsis.

CONCLUSION

The present study furnishes novel evidence elucidating the roles of GrpE1, HTRA2, ISCU, and CUP3 in the pathophysiology of sepsis. Such insights pave the way for a deeper understanding of the pathological mechanisms underpinning sepsis and hint at promising therapeutic strategies for the future.

Causal relationship of gut microbiota and metabolites on cognitive performance: A mendelian randomization analysis

Emerging evidence has indicated that the alterations in gut microbiota and metabolites are associated with cognitive performance. However, whether these associations imply a causal relationship remains to be definitively established. Here, we conducted two-sample mendelian randomization (MR) studies to explore the causal effects of gut microbiota and metabolites on cognitive performance, using large-scale genome-wide association studies (GWASs). We identified seven positive causalities between host genetic-driven gut microbiota and cognitive performance, including Class Clostridia (p = 0.0002), Order Clostridiales (p = 8.12E-05), Family Rhodospirillaceae (p = 0.042) and Ruminococcustorquesgroup (p = 0.030), Dialister (p = 0.027), Paraprevotella (p = 0.037) and RuminococcaceaeUCG003 (p = 0.007) at the genus level. Additionally, a total of four higher abundance of gut microbiota traits were identified to be negatively related to cognitive performance, including genus Blautia (p = 0.013), LachnospiraceaeFCS020group (p = 0.035), LachnospiraceaeNK4A136group (p = 0.034) and Roseburia (p = 0.00016). In terms of plasma metabolites, we discovered eight positive and six negative relationships between genetic liability in metabolites and cognitive performance (all p < 0.05). No evidence was detected across a series of sensitivity analyses, including pleiotropy and heterogeneity. Collectively, our MR analyses revealed that gut microbiota and metabolites were causally connected with cognitive performance, which holds significant potential for shedding light on the early detection and diagnosis of cognitive impairment, offering valuable insights into this area of research.

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.

ChREBP is activated by reductive stress and mediates GCKR-associated metabolic traits

Common genetic variants in glucokinase regulator (GCKR), which encodes GKRP, a regulator of hepatic glucokinase (GCK), influence multiple metabolic traits in genome-wide association studies (GWASs), making GCKR one of the most pleiotropic GWAS loci in the genome. It is unclear why. Prior work has demonstrated that GCKR influences the hepatic cytosolic NADH/NAD+ ratio, also referred to as reductive stress. Here, we demonstrate that reductive stress is sufficient to activate the transcription factor ChREBP and necessary for its activation by the GKRP-GCK interaction, glucose, and ethanol. We show that hepatic reductive stress induces GCKR GWAS traits such as increased hepatic fat, circulating FGF21, and circulating acylglycerol species, which are also influenced by ChREBP. We define the transcriptional signature of hepatic reductive stress and show its upregulation in fatty liver disease and downregulation after bariatric surgery in humans. These findings highlight how a GCKR-reductive stress-ChREBP axis influences multiple human metabolic traits.

Altered Gut Microbiota as a Potential Risk Factor for Coronary Artery Disease in Diabetes: A Two-Sample Bi-Directional Mendelian Randomization Study

The current body of research points to a notable correlation between an imbalance in gut microbiota and the development of type 2 diabetes mellitus (T2D) as well as its consequential ailment, coronary artery disease (CAD). The complexities underlying the association, especially in the context of diabetic coronary artery disease (DCAD), are not yet fully understood, and the causal links require further clarification. In this study, a bidirectional Mendelian randomization (MR) methodology was utilized to explore the causal relationships between gut microbiota, T2D, and CAD. By analyzing data from the DIAGRAM, GERA, UKB, FHS, and mibioGen cohorts and examining GWAS databases, we sought to uncover genetic variants linked to T2D, CAD, and variations in gut microbiota and metabolites, aiming to shed light on the potential mechanisms connecting gut microbiota with DCAD. Our investigation uncovered a marked causal link between the presence of Oxalobacter formigenes and an increased incidence of both T2D and CAD. Specifically, a ten-unit genetic predisposition towards T2D was found to be associated with a 6.1% higher probability of an increase in the Oxalobacteraceae family's presence (β = 0.061, 95% CI = 0.002-0.119). In a parallel finding, an augmented presence of Oxalobacter was related to an 8.2% heightened genetic likelihood of CAD (β = 0.082, 95% CI = 0.026-0.137). This evidence indicates a critical pathway by which T2D can potentially raise the risk of CAD via alterations in gut microbiota. Additionally, our analyses reveal a connection between CAD risk and Methanobacteria, thus providing fresh perspectives on the roles of TMAO and carnitine in the etiology of CAD. The data also suggest a direct causal relationship between increased levels of certain metabolites - proline, lysophosphatidylcholine, asparagine, and salicylurate - and the prevalence of both T2D and CAD. Sensitivity assessments reinforce the notion that changes in Oxalobacter formigenes could pose a risk for DCAD. There is also evidence to suggest that DCAD may, in turn, affect the gut microbiota's makeup. Notably, a surge in serum TMAO levels in individuals with CAD, coinciding with a reduced presence of methanogens, has been identified as a potentially significant factor for future examination.

Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results

Metabolite genome-wide association studies (mGWAS) have advanced our understanding of the genetic control of metabolite levels. However, interpreting these associations remains challenging due to a lack of tools to annotate gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we introduce the shortest reactional distance (SRD) metric, drawing from the comprehensive KEGG database, to enhance the biological interpretation of mGWAS results. We applied this approach to three independent mGWAS, including a case study on sickle cell disease patients. Our analysis reveals an enrichment of small SRD values in reported mGWAS pairs, with SRD values significantly correlating with mGWAS p values, even beyond the standard conservative thresholds. We demonstrate the utility of SRD annotation in identifying potential false negatives and inaccuracies within current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs, suitable to integrate statistical evidence to biological networks.

Genetically Predicted Causal Effects of Gut Microbiota and Gut Metabolites on Digestive Tract Cancer: A Two-Sample Mendelian Randomization Analysis

Background

Evidence from numerous observational studies and clinical trials has linked gut microbiota and metabolites to digestive tract cancer. However, the causal effect between these factors remains uncertain.

Methods

Data for this study were obtained from the MiBioGen, TwinsUK Registry, and FinnGen (version R8). Two-sample Mendelian randomization analysis with inverse variance weighting method was primarily used, and the results were validated by heterogeneity analysis, pleiotropy test, and sensitivity analysis.

Results

At P < 5 × 10-8, our analysis identified four gut microbiotas as risk factors for digestive tract cancer and six as risk factors for colorectal cancer. Conversely, one gut microbiota exhibited protection against bile duct cancer, and two showed protective effects against stomach cancer. At P < 1 × 10-5, our investigation revealed five, six, three, eight, eight, and eight gut microbiotas as risk factors for esophageal, stomach, bile duct, liver, pancreatic, and colorectal cancers, respectively. In contrast, four, two, eight, two, two, and five gut microbiotas exhibited protective effects against these cancers. Additionally, GABA, a metabolite of gut microbiota, displayed a significant protective effect against colorectal cancer.

Conclusion

In conclusion, specific gut microbiota and metabolites play roles as risk factors or protective factors for digestive tract cancer, and a causal relationship between them has been established, offering novel insights into gut microbiota-mediated cancer development.

The association between trimethylamine N-oxide levels and ischemic stroke occurrence: a meta-analysis and Mendelian randomization study

BACKGROUND

Trimethylamine-N-oxide (TMAO), an intestinal microbiota-derived choline metabolite, has been found to be associated with ischemic stroke (IS) in more and more studies. However, the causal role of TMAO on IS occurrence remains perplexing.

METHODS

We comprehensively screened the related clinical studies on PubMed, Web of Science, and Embase. Case-control and cohort studies that reported the TMAO levels of both IS patients and healthy controls were included, and the risk of bias was assessed according to the criteria by the Centre for Evidence-Based Medicine in Oxford, UK. A meta-analysis of the retrieved publications was performed with a random-effect model to analyze the connection between TMAO levels and IS events. Besides, a Mendelian randomization (MR) analysis was performed to study the causal effect of TMAO on IS, with pooled data of TMAO and IS obtained from genome-wide association studies (GWAS). The following methods were used: MR-Egger, weighted median, inverse-variance weighted, simple mode, and weighted mode. The study has been registered in INPLASY (Registration number: INPLASY2023100027).

RESULTS

Eight cohort or case-control studies covering 2444 cases and 1707 controls were identified. The pooled data indicated that the IS patients tended to have higher TMAO levels compared with the controls (mean difference: 1.97 μM; 95% confidence interval [CI]: 0.87, 3.07; P = 0.0005), while distinctive heterogeneity (I2 = 96%, P < 0.00001) was observed. Sub-group analysis revealed that the heterogeneity of the studies might be derived from the studies themselves. However, no causal effect of TMAO on IS was observed (P > 0.05) in the Mendelian randomization analysis of this study.

CONCLUSION

We confirmed that IS patients tend to have higher TMAO levels than healthy individuals, while our findings of MR analysis did not support the causal role of TMAO in IS occurrence. Therefore, more studies are required for a better understanding of the relationship between TMAO levels and IS onset.

Causal effect of gut-microbiota-derived metabolite trimethylamine N-oxide on Parkinson's disease: A Mendelian randomization study

BACKGROUND AND PURPOSE

It has been suggested that trimethylamine N-oxide (TMAO) is related to Parkinson's disease (PD) in observational studies. However, the direction of this association is inconsistent. An exploratory Mendelian randomization study was conducted to investigate whether TMAO and its precursors have a causal relationship with PD.

METHODS

Summary statistics were obtained for single nucleotide polymorphisms related to circulating levels of TMAO, betaine, carnitine and choline, and the corresponding data for the risk, age at onset and progression of PD from genome-wide association studies. Inverse-variance weighting was used as the primary method for effect estimation. The false discovery rate was applied to the correction of multiple testing. A p value of association <0.05 but above the false discovery rate corrected threshold was deemed suggestive evidence of a possible association. A range of robust Mendelian randomization methods were used for sensitivity analysis.

RESULTS

Suggestive evidence was observed of an inverse causal effect of TMAO on motor fluctuations (odds ratio [OR] 0.851, 95% confidence interval [CI] 0.731, 0.990, p = 0.037) and carnitine on insomnia (OR 0.817, 95% CI 0.700, 0.954, p = 0.010) and a positive causal effect of betaine on Hoehn-Yahr stage (OR 1.397, 95% CI 1.112, 1.756, p = 0.004), Unified Parkinson's Disease Rating Scale (UPDRS) III score (β = 0.138, 95% CI 0.051, 0.225, p = 0.002), motor fluctuations (OR 1.236, 95% CI 1.011, 1.511, p = 0.039), and choline on UPDRS IV (β = 0.106, 95% CI 0.026, 0.185, p = 0.009) and modified Schwab and England Activities of Daily Living Scale score (β = 0.806, 95% CI 0.127, 1.484, p = 0.020).

CONCLUSIONS

Our findings provide suggestive evidence that TMAO and its precursors have a causal effect on the progression of PD. Further investigation of the underlying mechanisms is required.

Associations between gut microbiota and Parkinson disease: A bidirectional Mendelian randomization analysis

BACKGROUND AND PURPOSE

Parkinson disease (PD)-associated alterations in the gut microbiome have been observed in clinical and animal studies. However, it remains unclear whether this association reflects a causal effect in humans.

METHODS

We performed two-sample bidirectional Mendelian randomization using summary statistics from the international consortium MiBioGen (N = 18,340), the Framingham Heart Study (N = 2076), and the International Parkinson's Disease Genomics Consortium for PD (33,674 cases and 449,056 controls) and PD age at onset (17,996 cases).

RESULTS

Twelve microbiota features presented suggestive associations with PD risk or age at onset. Genetically increased Bifidobacterium levels correlated with decreased PD risk (odds ratio = 0.77, 95% confidence interval [CI] = 0.60-0.99, p = 0.040). Conversely, high levels of five short-chain fatty acid (SCFA)-producing bacteria (LachnospiraceaeUCG010, RuminococcaceaeUCG002, Clostridium sensustricto1, Eubacterium hallii group, and Bacillales) correlated with increased PD risk, and three SCFA-producing bacteria (Roseburia, RuminococcaceaeUCG002, and Erysipelatoclostridium) correlated with an earlier age at PD onset. Gut production of serotonin was associated with an earlier age at PD onset (beta = -0.64, 95% CI = -1.15 to -0.13, p = 0.013). In the reverse direction, genetic predisposition to PD was related to altered gut microbiota composition.

CONCLUSIONS

These results support a bidirectional relationship between gut microbiome dysbiosis and PD, and highlight the role of elevated endogenous SCFAs and serotonin in PD pathogenesis. Future clinical studies and experimental evidence are needed to explain the observed associations and to suggest new therapeutic approaches, such as dietary probiotic supplementation.

Genome-wide association analysis of plasma lipidome identifies 495 genetic associations

The human plasma lipidome captures risk for cardiometabolic diseases. To discover new lipid-associated variants and understand the link between lipid species and cardiometabolic disorders, we perform univariate and multivariate genome-wide analyses of 179 lipid species in 7174 Finnish individuals. We fine-map the associated loci, prioritize genes, and examine their disease links in 377,277 FinnGen participants. We identify 495 genome-trait associations in 56 genetic loci including 8 novel loci, with a considerable boost provided by the multivariate analysis. For 26 loci, fine-mapping identifies variants with a high causal probability, including 14 coding variants indicating likely causal genes. A phenome-wide analysis across 953 disease endpoints reveals disease associations for 40 lipid loci. For 11 coronary artery disease risk variants, we detect strong associations with lipid species. Our study demonstrates the power of multivariate genetic analysis in correlated lipidomics data and reveals genetic links between diseases and lipid species beyond the standard lipids.

Genotype-based "virtual" metabolomics in a clinical biobank identifies novel metabolite-disease associations

Circulating metabolites act as biomarkers of dysregulated metabolism, and may inform disease pathophysiology. A portion of the inter-individual variability in circulating metabolites is influenced by common genetic variation. We evaluated whether a genetics-based "virtual" metabolomics approach can identify novel metabolite-disease associations. We examined the association between polygenic scores for 726 metabolites (derived from OMICSPRED) with 1,247 clinical phenotypes in 57,735 European ancestry and 15,754 African ancestry participants from the BioVU DNA Biobank. We probed significant relationships through Mendelian randomization (MR) using genetic instruments constructed from the METSIM Study, and validated significant MR associations using independent GWAS of candidate phenotypes. We found significant associations between 336 metabolites and 168 phenotypes in European ancestry and 107 metabolites and 56 phenotypes among African ancestry. Of these metabolite-disease pairs, MR analyses confirmed associations between 73 metabolites and 53 phenotypes in European ancestry. Of 22 metabolite-phenotype pairs evaluated for replication in independent GWAS, 16 were significant (false discovery rate p<0.05). Validated findings included the metabolites bilirubin and X-21796 with cholelithiasis, phosphatidylcholine(16:0/22:5n3,18:1/20:4) and arachidonate(20:4n6) with inflammatory bowel disease and Crohn's disease, and campesterol with coronary artery disease and myocardial infarction. These associations may represent biomarkers or potentially targetable mediators of disease risk.

Genotype-based "virtual" metabolomics in a clinical biobank identifies novel metabolite-disease associations

Circulating metabolites act as biomarkers of dysregulated metabolism, and may inform disease pathophysiology. A portion of the inter-individual variability in circulating metabolites is influenced by common genetic variation. We evaluated whether a genetics-based "virtual" metabolomics approach can identify novel metabolite-disease associations. We examined the association between polygenic scores for 726 metabolites (derived from OMICSPRED) with 1,247 clinical phenotypes in 57,735 European ancestry and 15,754 African ancestry participants from the BioVU DNA Biobank. We probed significant relationships through Mendelian randomization (MR) using genetic instruments constructed from the METSIM Study, and validated significant MR associations using independent GWAS of candidate phenotypes. We found significant associations between 336 metabolites and 168 phenotypes in European ancestry and 107 metabolites and 56 phenotypes among African ancestry. Of these metabolite-disease pairs, MR analyses confirmed associations between 73 metabolites and 53 phenotypes in European ancestry. Of 22 metabolite-phenotype pairs evaluated for replication in independent GWAS, 16 were significant (false discovery rate p<0.05). Validated findings included the metabolites bilirubin and X-21796 with cholelithiasis, phosphatidylcholine(16:0/22:5n3,18:1/20:4) and arachidonate(20:4n6) with inflammatory bowel disease and Crohn's disease, and campesterol with coronary artery disease and myocardial infarction. These associations may represent biomarkers or potentially targetable mediators of disease risk.

Causal associations of gut microbiota and metabolites on sepsis: a two-sample Mendelian randomization study

Background

Sepsis stands as a dire medical condition, arising when the body's immune response to infection spirals into overdrive, paving the way for potential organ damage and potential mortality. With intestinal flora's known impact on sepsis but a dearth of comprehensive data, our study embarked on a two-sample Mendelian randomization analysis to probe the causal link between gut microbiota and their metabolites with severe sepsis patients who succumbed within a 28-day span.

Methods

Leveraging data from Genome-wide association study (GWAS) and combining it with data from 2,076 European descendants in the Framingham Heart Study, single-nucleotide polymorphisms (SNPs) were employed as Instrumental Variables (IVs) to discern gene loci affiliated with metabolites. GWAS summary statistics for sepsis were extracted from the UK Biobank consortium.

Results

In this extensive exploration, 93 distinct genome-wide significant SNPs correlated with gut microbial metabolites and specific bacterial traits were identified for IVs construction. Notably, a substantial link between Coprococcus2 and both the incidence (OR of 0.80, 95% CI: 0.68-0.94, P=0.007) and the 28-day mortality rate (OR 0.48, 95% CI: 0.27-0.85, P=0.013) of sepsis was observed. The metabolite α-hydroxybutyrate displayed a marked association with sepsis onset (OR=1.08, 95% CI: 1.02-1.15, P=0.006) and its 28-day mortality rate (OR=1.17, 95% CI: 1.01-1.36, P=0.029).

Conclusion

This research unveils the intricate interplay between the gut microbial consortium, especially the genus Coprococcus, and the metabolite α-hydroxybutyrate in the milieu of sepsis. The findings illuminate the pivotal role of intestinal microbiota and their metabolites in sepsis' pathogenesis, offering fresh insights for future research and hinting at novel strategies for sepsis' diagnosis, therapeutic interventions, and prognostic assessments.

Protein-metabolite association studies identify novel proteomic determinants of metabolite levels in human plasma

Although many novel gene-metabolite and gene-protein associations have been identified using high-throughput biochemical profiling, systematic studies that leverage human genetics to illuminate causal relationships between circulating proteins and metabolites are lacking. Here, we performed protein-metabolite association studies in 3,626 plasma samples from three human cohorts. We detected 171,800 significant protein-metabolite pairwise correlations between 1,265 proteins and 365 metabolites, including established relationships in metabolic and signaling pathways such as the protein thyroxine-binding globulin and the metabolite thyroxine, as well as thousands of new findings. In Mendelian randomization (MR) analyses, we identified putative causal protein-to-metabolite associations. We experimentally validated top MR associations in proof-of-concept plasma metabolomics studies in three murine knockout strains of key protein regulators. These analyses identified previously unrecognized associations between bioactive proteins and metabolites in human plasma. We provide publicly available data to be leveraged for studies in human metabolism and disease.

metGWAS 1.0: an R workflow for network-driven over-representation analysis between independent metabolomic and meta-genome-wide association studies

MOTIVATION

The method of genome-wide association studies (GWAS) and metabolomics combined provide an quantitative approach to pinpoint metabolic pathways and genes linked to specific diseases; however, such analyses require both genomics and metabolomics datasets from the same individuals/samples. In most cases, this approach is not feasible due to high costs, lack of technical infrastructure, unavailability of samples, and other factors. Therefore, an unmet need exists for a bioinformatics tool that can identify gene loci-associated polymorphic variants for metabolite alterations seen in disease states using standalone metabolomics.

RESULTS

Here, we developed a bioinformatics tool, metGWAS 1.0, that integrates independent GWAS data from the GWAS database and standalone metabolomics data using a network-based systems biology approach to identify novel disease/trait-specific metabolite-gene associations. The tool was evaluated using standalone metabolomics datasets extracted from two metabolomics-GWAS case studies. It discovered both the observed and novel gene loci with known single nucleotide polymorphisms when compared to the original studies.

AVAILABILITY AND IMPLEMENTATION

The developed metGWAS 1.0 framework is implemented in an R pipeline and available at: https://github.com/saifurbd28/metGWAS-1.0.

Trimethylamine N-oxide and risk of inflammatory bowel disease: A Mendelian randomization study

A previous study suggested that inflammatory bowel disease (IBD) patients have low plasma levels of trimethylamine N-oxide (TMAO). In the present study, we examined this hypothesis using Mendelian randomization analysis. We used summary statistics data for single-nucleotide polymorphisms associated with plasma levels of TMAO, and the corresponding data for IBD from a genome-wide association meta-analysis of 59,957 individuals (25,042 diagnosed IBD cases, 34,915 controls). The association between genetically predicted plasma TMAO levels and IBD showed odds ratios (95% confidence interval [CI]) per 1 interquartile range increment (per 2.4 μmol/L) in TMAO levels were 0.91 (0.81-1.01, P = .084) for IBD, 0.88 (0.76-1.02, P = .089) for ulcerative colitis, 0.91 (0.79-1.05, P = .210) for Crohn disease. There was no evidence for pleiotropy based on the Mendelian randomization-Egger regression analyses (P-intercept = 0.669 for IBD). Further investigations would be needed to understand the causal relationship between TMAO and IBD.

Gut Microbiota and Respiratory Infections: Insights from Mendelian Randomization

The role of the gut microbiota in modulating the risk of respiratory infections has garnered increasing attention. However, conventional clinical trials have faced challenges in establishing the precise relationship between the two. In this study, we conducted a Mendelian randomization analysis with single nucleotide polymorphisms employed as instrumental variables to assess the causal links between the gut microbiota and respiratory infections. Two categories of bacteria, family Lactobacillaceae and genus Family XIII AD3011, were causally associated with the occurrence of upper respiratory tract infections (URTIs). Four categories of gut microbiota existed that were causally associated with lower respiratory tract infections (LRTIs), with order Bacillales and genus Paraprevotella showing a positive association and genus Alistipes and genus Ruminococcaceae UCG009 showing a negative association. The metabolites and metabolic pathways only played a role in the development of LRTIs, with the metabolite deoxycholine acting negatively and menaquinol 8 biosynthesis acting positively. The identification of specific bacterial populations, metabolites, and pathways may provide new clues for mechanism research concerning therapeutic interventions for respiratory infections. Future research should focus on elucidating the potential mechanisms regulating the gut microbiota and developing effective strategies to reduce the incidence of respiratory infections. These findings have the potential to significantly improve global respiratory health.

Multiset correlation and factor analysis enables exploration of multi-omics data

Multi-omics datasets are becoming more common, necessitating better integration methods to realize their revolutionary potential. Here, we introduce multi-set correlation and factor analysis (MCFA), an unsupervised integration method tailored to the unique challenges of high-dimensional genomics data that enables fast inference of shared and private factors. We used MCFA to integrate methylation markers, protein expression, RNA expression, and metabolite levels in 614 diverse samples from the Trans-Omics for Precision Medicine/Multi-Ethnic Study of Atherosclerosis multi-omics pilot. Samples cluster strongly by ancestry in the shared space, even in the absence of genetic information, while private spaces frequently capture dataset-specific technical variation. Finally, we integrated genetic data by conducting a genome-wide association study (GWAS) of our inferred factors, observing that several factors are enriched for GWAS hits and trans-expression quantitative trait loci. Two of these factors appear to be related to metabolic disease. Our study provides a foundation and framework for further integrative analysis of ever larger multi-modal genomic datasets.

A genome-wide association study identifies 41 loci associated with eicosanoid levels

Eicosanoids are biologically active derivatives of polyunsaturated fatty acids with broad relevance to health and disease. We report a genome-wide association study in 8406 participants of the Atherosclerosis Risk in Communities Study, identifying 41 loci associated with 92 eicosanoids and related metabolites. These findings highlight loci required for eicosanoid biosynthesis, including FADS1-3, ELOVL2, and numerous CYP450 loci. In addition, significant associations implicate a range of non-oxidative lipid metabolic processes in eicosanoid regulation, including at PKD2L1/SCD and several loci involved in fatty acyl-CoA metabolism. Further, our findings highlight select clearance mechanisms, for example, through the hepatic transporter encoded by SLCO1B1. Finally, we identify eicosanoids associated with aspirin and non-steroidal anti-inflammatory drug use and demonstrate the substantial impact of genetic variants even for medication-associated eicosanoids. These findings shed light on both known and unknown aspects of eicosanoid metabolism and motivate interest in several gene-eicosanoid associations as potential functional participants in human disease.

Exploring the Complex Relationship between Gut Microbiota and Risk of Colorectal Neoplasia Using Bidirectional Mendelian Randomization Analysis

BACKGROUND

Human gut microbiome has complex relationships with the host, contributing to metabolism, immunity, and carcinogenesis.

METHODS

Summary-level data for gut microbiota and metabolites were obtained from MiBioGen, FINRISK and human metabolome consortia. Summary-level data for colorectal cancer were derived from a genome-wide association study meta-analysis. In forward Mendelian randomization (MR), we employed genetic instrumental variables (IV) for 24 gut microbiota taxa and six bacterial metabolites to examine their causal relationship with colorectal cancer. We also used a lenient threshold for nine apriori gut microbiota taxa as secondary analyses. In reverse MR, we explored association between genetic liability to colorectal neoplasia and abundance of microbiota studied above using 95, 19, and 7 IVs for colorectal cancer, adenoma, and polyps, respectively.

RESULTS

Forward MR did not find evidence indicating causal relationship between any of the gut microbiota taxa or six bacterial metabolites tested and colorectal cancer risk. However, reverse MR supported genetic liability to colorectal adenomas was causally related with increased abundance of two taxa: Gammaproteobacteria (β = 0.027, which represents a 0.027 increase in log-transformed relative abundance values of Gammaproteobacteria for per one-unit increase in log OR of adenoma risk; P = 7.06×10-8), Enterobacteriaceae (β = 0.023, P = 1.29×10-5).

CONCLUSIONS

We find genetic liability to colorectal neoplasia may be associated with abundance of certain microbiota taxa. It is more likely that subset of colorectal cancer genetic liability variants changes gut biology by influencing both gut microbiota and colorectal cancer risk.

IMPACT

This study highlights the need of future complementary studies to explore causal mechanisms linking both host genetic variation with gut microbiome and colorectal cancer susceptibility.

Whole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations

Circulating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease.