Metabolome-wide Association Study Research Articles

Metabolomics signatures of air pollution mixtures and lung cancer risk: Large-Scale Metabolome-Wide Association Study in the Cancer Prevention Study Cohorts

Metabolomics signatures of air pollution mixtures and lung cancer risk: Large-Scale Metabolome-Wide Association Study in the Cancer Prevention Study Cohorts

Prospective metabolome-wide association study suggests environmental chemicals cause early-onset breast cancer through disruption of nutrient sensing mechanisms

Prospective metabolome-wide association study suggests environmental chemicals cause early-onset breast cancer through disruption of nutrient sensing mechanisms

Metabolic signatures of population exposure to metal mixtures: A metabolome-wide association study

Numerous studies have explored the health impacts of individual metal exposures, yet the effects of metal mixtures on human endogenous metabolism remain largely unexplored. We aimed to assess the serum metabolic signatures of people exposed to metal mixtures. Serum and urine samples were collected from 186 workers at a steel factory in Anhui, China, in September 2019. Inductively coupled plasma mass spectrometry was used to analyze the concentrations of 23 metal elements. The serum metabolome was determined by liquid chromatography-mass spectrometry (LC-MS). A metabolome-wide association study (MWAS) was performed across the metal exposures and metabolism using quantile g-computation modeling. Pathway enrichment analysis was performed using MetaboAnalyst. We identified 226 metabolites associated with metal mixtures, primarily involving lipid metabolism (glycerophospholipids, sphingolipids), amino acid metabolism (arginine and proline, alanine, aspartate and glutamate metabolism) and caffeine metabolic pathways. Exposure to metal mixtures is mainly associated with alterations in lipid metabolism and amino acid metabolism, particularly in the glycerophospholipid and arginine and proline metabolism pathways.

Metabolomics signatures of air pollution mixtures and lung cancer risk: A large-scale metabolome-wide association study in the Cancer Prevention Study cohorts.

10571 Background: Despite the well-established link between air pollution exposure and lung carcinogenesis, the underlying mechanism remains underexplored. While metabolomics has provided valuable molecular insights, existing air pollution metabolomics studies only assess biological changes to single air pollutants, without examining the join effects of air pollutant mixtures. We sought to identify metabolic signatures mediating the association between ambient air pollution mixtures and lung cancer risk. Methods: A total of 603 matched lung cancer case-control pairs were selected from the Cancer Prevention Study cohorts. Controls were matched to cases on age at blood draw, sex, race/ethnicity, and blood draw date. Plasma metabolomics was conducted using ultrahigh-performance liquid chromatography-tandem mass spectrometry. Air pollution mixtures include carbon monoxide, nitrogen dioxide, particulate matter, sulfur dioxide, and ozone, matched to subjects’ residential address at blood draw. We conducted an untargeted metabolome-wide association study with multivariable-adjusted linear and conditional logistic regression models to assess associations between metabolites and individual air pollutants and lung cancer risk, respectively, adjusting for individual and lifestyle-related covariates. We used a meet-in-the-middle (MITM) approach to identify potential mediators. Quantile g-computation was used to examine the effects of air pollution mixtures on metabolome perturbations to analyze the collective effects of multiple air pollutants concurrently. Results: Among 1,138 extracted metabolic features, 162 were significantly associated with the combined mixture (false discovery rate < 0.2). Using MITM, no metabolites were associated with both air pollution mixture and lung cancer risk at FDR < 0.2. At raw p < 0.05, 16 metabolites were identified as potential mediators of the air pollution mixture and lung cancer risk. These metabolites, including 4-hydroxyphenylacetylglutamine, glutamate, and 3-aminoisobutyrate, were also significantly associated with the mixture at FDR < 0.2. Conclusions: Findings from this analysis demonstrate the feasibility of considering multiple air pollutants as mixtures when conducting air pollution-related metabolomics investigations. Future exploration of the underlying biological mechanisms will be critical for validating pathways that participate in air pollution carcinogenicity. As lung cancer cases in never smokers continue to increase, it is pertinent to study air pollution and other environmental risk factors on cancer incidence.

Associations of Urine and Plasma Metabolites With Kidney Failure and Death in a Chronic Kidney Disease Cohort

Rationale & ObjectiveBiomarkers that enable better identification of persons with chronic kidney disease (CKD) who are at higher risk for disease progression and adverse events are needed. This study sought to identify urine and plasma metabolites associated with progression of kidney disease. Study DesignProspective metabolome-wide association study. Setting & ParticipantsPersons with CKD enrolled in the German CKD Study (GCKD) with metabolite measurements; with external validation within the Atherosclerosis Risk in Communities Study. Exposures1,513 urine and 1,416 plasma metabolites (Metabolon, Inc.) measured at study entry using untargeted mass spectrometry. OutcomesMain endpoints were kidney failure (KF), and a composite endpoint of KF, eGFR <15 mL/min/1.73m2, or 40% decline in eGFR (CKE). Death from any cause was a secondary endpoint. After a median of 6.5 years follow-up, 500 persons experienced KF, 1,083 experienced CKE and 680 died. Analytical ApproachTime-to-event analyses using multivariable proportional hazard regression models in a discovery-replication design, with external validation. Results5,088 GCKD participants were included in analyses of urine metabolites and 5,144 in analyses of plasma metabolites. Among 182 unique metabolites, 30 were significantly associated with KF, 49 with CKE, and 163 with death. The strongest association with KF was observed for plasma hydroxyasparagine (hazard ratio: 1.95, 95% confidence interval: 1.68-2.25). An unnamed metabolite measured in plasma and urine was significantly associated with KF, CKE, and death. External validation of the identified associations of metabolites with KF or CKE revealed direction-consistency for 88% of observed associations. Selected associations of 18 metabolites with study outcomes have not been previously reported. LimitationsUse of observational data and semi-quantitative metabolite measurements at a single time point. ConclusionsThe observed associations between metabolites and KF, CKE or death in persons with CKD confirmed previously reported findings and also revealed several associations not previously described. These findings warrant confirmatory research in other study cohorts.

Human Milk Composition Is Associated with Maternal Body Mass Index in a Cross-Sectional, Untargeted Metabolomics Analysis of Human Milk from Guatemalan Mothers

BackgroundMaternal overweight and obesity has been associated with poor lactation performance including delayed lactogenesis and reduced duration. However, the effect on human milk composition is less well understood. ObjectivesWe evaluated the relationship of maternal BMI on the human milk metabolome among Guatemalan mothers. MethodsWe used data from 75 Guatemalan mothers who participated in the Household Air Pollution Intervention Network trial. Maternal BMI was measured between 9 and <20 weeks of gestation. Milk samples were collected at a single time point using aseptic collection from one breast at 6 mo postpartum and analyzed using high-resolution mass spectrometry. A cross-sectional untargeted high-resolution metabolomics analysis was performed by coupling hydrophilic interaction liquid chromatography (HILIC) and reverse phase C18 chromatography with mass spectrometry. Metabolic features associated with maternal BMI were determined by a metabolome-wide association study (MWAS), adjusting for baseline maternal age, education, and dietary diversity, and perturbations in metabolic pathways were identified by pathway enrichment analysis. ResultsThe mean age of participants at baseline was 23.62 ± 3.81 y, and mean BMI was 24.27 ± 4.22 kg/m2. Of the total metabolic features detected by HILIC column (19,199 features) and by C18 column (11,594 features), BMI was associated with 1026 HILIC and 500 C18 features. Enriched pathways represented amino acid metabolism, galactose metabolism, and xenobiotic metabolic metabolism. However, no significant features were identified after adjusting for multiple comparisons using the Benjamini–Hochberg false discovery rate procedure (FDRBH < 0.2). ConclusionsFindings from this untargeted MWAS indicate that maternal BMI is associated with metabolic perturbations of galactose metabolism, xenobiotic metabolism, and xenobiotic metabolism by cytochrome p450 and biosynthesis of amino acid pathways. Significant metabolic pathway alterations detected in human milk were associated with energy metabolism-related pathways including carbohydrate and amino acid metabolism.This trial was registered at clinicaltrials.gov as NCT02944682.

A comprehensive multiplatform metabolomic analysis reveals alterations of 2-hydroxybutyric acid among women with deep endometriosis related to the pesticide trans-nonachlor

BackgroundExposure to persistent organic pollutants (POPs) has been related to the risk of endometriosis however the mechanisms remain unclear. The objective of the present study was to characterize the metabolic profiles underpinning the associations between POPs and endometriosis risk. MethodologyA hospital-based case-control study was conducted in France to recruit women with and without surgically confirmed deep endometriosis. Women's serum was analyzed using gas and liquid chromatography coupled to high-resolution mass spectrometry (HRMS) to measure the levels of polychlorinated biphenyls (PCBs), organochlorinated pesticides (OCPs) and per-/polyfluoroalkyl substances (PFAS). A comprehensive metabolomic profiling was conducted using targeted HRMS and 1H nuclear magnetic resonance (1H NMR) to cover polar and non-polar fractions. A “meet-in-the-middle” statistical framework was applied to identify the metabolites related to endometriosis and POP levels, using multivariate linear and logistic regressions adjusting for confounding variables. ResultsFourteen PCBs, six OCPs and six PFAS were widely found in almost all serum samples. The pesticide trans-nonachlor was the POP most strongly and positively associated with deep endometriosis risk, with odds ratio (95 % confidence interval) of 2.42 (1.49; 4.12), followed by PCB180 and 167. Women with endometriosis exhibited a distinctive metabolic profile, with elevated serum levels of lactate, ketone bodies and multiple amino acids and lower levels of bile acids, phosphatidylcholines (PCs), cortisol and hippuric acid. The metabolite 2-hydroxybutyrate was simultaneously associated to endometriosis risk and exposure to trans-nonachlor. ConclusionsTo the best of our knowledge, this is the first comprehensive metabolome-wide association study of endometriosis, integrating ultra-trace profiling of POPs. The results confirmed a metabolic alteration among women with deep endometriosis that could be also associated to the exposure to POPs. Further observational and experimental studies will be required to delineate the causal ordering of those associations and gain insight on the underlying mechanisms.

Metabolomic signatures of ideal cardiovascular health in black adults

Plasma metabolomics profiling is an emerging methodology to identify metabolic pathways underlying cardiovascular health (CVH). The objective of this study was to define metabolomic profiles underlying CVH in a cohort of Black adults, a population that is understudied but suffers from disparate levels of CVD risk factors. The Morehouse-Emory Cardiovascular (MECA) Center for Health Equity study cohort consisted of 375 Black adults (age 53 ± 10, 39% male) without known CVD. CVH was determined by the AHA Life’s Simple 7 (LS7) score, calculated from measured blood pressure, body mass index (BMI), fasting blood glucose and total cholesterol, and self-reported physical activity, diet, and smoking. Plasma metabolites were assessed using untargeted high-resolution metabolomics profiling. A metabolome wide association study (MWAS) identified metabolites associated with LS7 score after adjusting for age and sex. Using Mummichog software, metabolic pathways that were significantly enriched in metabolites associated with LS7 score were identified. Metabolites representative of these pathways were compared across clinical domains of LS7 score and then developed into a metabolomics risk score for prediction of CVH. We identified novel metabolomic signatures and pathways associated with CVH in a cohort of Black adults without known CVD. Representative and highly prevalent metabolites from these pathways included glutamine, glutamate, urate, tyrosine and alanine, the concentrations of which varied with BMI, fasting glucose, and blood pressure levels. When assessed in conjunction, these metabolites were independent predictors of CVH. One SD increase in the novel metabolomics risk score was associated with a 0.88 higher LS7 score, which translates to a 10.4% lower incident CVD risk. We identified novel metabolomic signatures of ideal CVH in a cohort of Black Americans, showing that a core group of metabolites central to nitrogen balance, bioenergetics, gluconeogenesis, and nucleotide synthesis were associated with CVH in this population.

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.

Dioxin(-like)-Related Biological Effects through Integrated Chemical-wide and Metabolome-wide Analyses.

Dioxin(-like) exposures are linked to adverse health effects, including cancer. However, metabolic alterations induced by these chemicals remain largely unknown. Beyond known dioxin(-like) compounds, we leveraged a chemical-wide approach to assess chlorinated co-exposures and parent compound products [termed dioxin(-like)-related compounds] among 137 occupational workers. Endogenous metabolites were profiled by untargeted metabolomics, namely, reversed-phase chromatography with negative electrospray ionization (C18-negative) and hydrophilic interaction liquid chromatography with positive electrospray ionization (HILIC-positive). We performed a metabolome-wide association study to select dioxin(-like) associated metabolic features using a 20% false discovery rate threshold. Metabolic features were then characterized by pathway enrichment analyses. There are no significant features associated with polychlorinated dibenzo-p-dioxins (PCDDs), a subgroup of known dioxin(-like) compounds. However, 3,110 C18-negative and 2,894 HILIC-positive features were associated with at least one of the PCDD-related compounds. Abundant metabolic changes were also observed for polychlorinated dibenzofuran-related and polychlorinated biphenyl-related compounds. These metabolic features were primarily enriched in pathways of amino acids, lipid and fatty acids, carbohydrates, cofactors, and nucleotides. Our study highlights the potential of chemical-wide analysis for comprehensive exposure assessment beyond targeted chemicals. Coupled with advanced endogenous metabolomics, this approach allows for an in-depth exploration of metabolic alterations induced by environmental chemicals.

Overall and individual associations between per- and polyfluoroalkyl substances and liver function indices and the metabolic mechanism

Per- and polyfluoroalkyl substances (PFAS) can disrupt liver homeostasis. Studies have shown that a single exposure to PFAS may provoke abnormal liver function; however, few studies have investigated the overall effect of PFAS mixtures. We aimed to investigate associations between exposure to PFAS mixtures and liver function indices and explore the relevant mechanisms. This study included 278 adult males from Guangzhou, China. Serum metabolite profiles were analyzed using untargeted metabolomics. We applied weighted quantile sum (WQS) regression as well as Bayesian kernel machine regression (BKMR) to analyze the association of nine PFAS mixtures with 14 liver function indices. PFAS mixtures were positively associated with apolipoprotein B (APOB) and gamma-glutamyltransferase (GGT) and negatively associated with direct bilirubin (DBIL) and total bilirubin (TBIL) in both the WQS and BKMR analyses. In addition, Spearman’s correlation test showed individual PFAS correlated with APOB, GGT, TBIL, and DBIL, while there’s little correlation between individual PFAS and other liver function indices. In linear regression analysis, PFHxS, PFOS, PFHpS, PFNA, PFDA, and PFUdA were associated with APOB; PFOA, PFDA, PFOS, PFNA, and PFUdA were associated with GGT. Subsequently, a metabolome-wide association study and mediation analysis were combined to explore metabolites that mediate these associations. The mechanisms linking PFAS to APOB and GGT are mainly related with amino acid and glycerophospholipid metabolism. High-dimensional mediation analysis showed that glycerophospholipids are the main markers of the association between PFAS and APOB, and that (R)-dihydromaleimide, Ile Leu, (R)-(+)-2-pyrrolidone-5-carboxylic acid, and L-glutamate are the main markers of the association between PFAS and GGT. In summary, overall associations between PFAS and specific indices of liver function were found using two statistical methods; the metabolic pathways and markers identified here may serve to prompt more detailed study in animal-based systems, as well as a similar detailed analysis in other populations.

Untargeted serum metabolomics reveals novel metabolite associations and disruptions in amino acid and lipid metabolism in Parkinson’s disease

BackgroundUntargeted high-resolution metabolomic profiling provides simultaneous measurement of thousands of metabolites. Metabolic networks based on these data can help uncover disease-related perturbations across interconnected pathways.ObjectiveIdentify metabolic disturbances associated with Parkinson’s disease (PD) in two population-based studies using untargeted metabolomics.MethodsWe performed a metabolome-wide association study (MWAS) of PD using serum-based untargeted metabolomics data derived from liquid chromatography with high-resolution mass spectrometry (LC-HRMS) using two distinct population-based case-control populations. We also combined our results with a previous publication of 34 metabolites linked to PD in a large-scale, untargeted MWAS to assess external validation.ResultsLC-HRMS detected 4,762 metabolites for analysis (HILIC: 2716 metabolites; C18: 2046 metabolites). We identified 296 features associated with PD at FDR<0.05, 134 having a log2 fold change (FC) beyond ±0.5 (228 beyond ±0.25). Of these, 104 were independently associated with PD in both discovery and replication studies at p<0.05 (170 at p<0.10), while 27 were associated with levodopa-equivalent dose among the PD patients. Intriguingly, among the externally validated features were the microbial-related metabolites, p-cresol glucuronide (FC=2.52, 95% CI=1.67, 3.81, FDR=7.8e-04) and p-cresol sulfate. P-cresol glucuronide was also associated with motor symptoms among patients. Additional externally validated metabolites associated with PD include phenylacetyl-L-glutamine, trigonelline, kynurenine, biliverdin, and pantothenic acid. Novel associations include the anti-inflammatory metabolite itaconate (FC=0.79, 95% CI=0.73, 0.86; FDR=2.17E-06) and cysteine-S-sulfate (FC=1.56, 95% CI=1.39, 1.75; FDR=3.43E-11). Seventeen pathways were enriched, including several related to amino acid and lipid metabolism.ConclusionsOur results revealed PD-associated metabolites, confirming several previous observations, including for p-cresol glucuronide, and newly implicating interesting metabolites, such as itaconate. Our data also suggests metabolic disturbances in amino acid and lipid metabolism and inflammatory processes in PD.

The citrate transporter SLC13A5 as a therapeutic target for kidney disease: evidence from Mendelian randomization to inform drug development

BackgroundSolute carrier family 13 member 5 (SLC13A5) is a Na+-coupled citrate co-transporter that mediates entry of extracellular citrate into the cytosol. SLC13A5 inhibition has been proposed as a target for reducing progression of kidney disease. The aim of this study was to leverage the Mendelian randomization paradigm to gain insight into the effects of SLC13A5 inhibition in humans, towards prioritizing and informing clinical development efforts.MethodsThe primary Mendelian randomization analyses investigated the effect of SLC13A5 inhibition on measures of kidney function, including creatinine and cystatin C-based measures of estimated glomerular filtration rate (creatinine-eGFR and cystatin C-eGFR), blood urea nitrogen (BUN), urine albumin-creatinine ratio (uACR), and risk of chronic kidney disease and microalbuminuria. Secondary analyses included a paired plasma and urine metabolome-wide association study, investigation of secondary traits related to SLC13A5 biology, a phenome-wide association study (PheWAS), and a proteome-wide association study. All analyses were compared to the effect of genetically predicted plasma citrate levels using variants selected from across the genome, and statistical sensitivity analyses robust to the inclusion of pleiotropic variants were also performed. Data were obtained from large-scale genetic consortia and biobanks, with sample sizes ranging from 5023 to 1,320,016 individuals.ResultsWe found evidence of associations between genetically proxied SLC13A5 inhibition and higher creatinine-eGFR (p = 0.002), cystatin C-eGFR (p = 0.005), and lower BUN (p = 3 × 10−4). Statistical sensitivity analyses robust to the inclusion of pleiotropic variants suggested that these effects may be a consequence of higher plasma citrate levels. There was no strong evidence of associations of genetically proxied SLC13A5 inhibition with uACR or risk of CKD or microalbuminuria. Secondary analyses identified evidence of associations with higher plasma calcium levels (p = 6 × 10−13) and lower fasting glucose (p = 0.02). PheWAS did not identify any safety concerns.ConclusionsThis Mendelian randomization analysis provides human-centric insight to guide clinical development of an SLC13A5 inhibitor. We identify plasma calcium and citrate as biologically plausible biomarkers of target engagement, and plasma citrate as a potential biomarker of mechanism of action. Our human genetic evidence corroborates evidence from various animal models to support effects of SLC13A5 inhibition on improving kidney function.

Cerebrospinal fluid metabolome‐wide association study of Alzheimer’s disease provides insight into molecular pathways

AbstractBackgroundMany genetic risk variants have been identified for Alzheimer’s disease (AD), but their functional consequence on molecular pathways are unclear. Quantitative trait loci (QTL) studies help elucidate the functional effect of genetic variance on (neuro)biological processes. The understanding of AD mechanisms has been limited due to the relative inaccessibility of in vivo brain tissue and its surroundings. In this study, we integrated genetic and metabolomics cerebrospinal fluid (CSF) data, to understand how AD risk variants influence neurobiological processes.MethodWe performed a genome‐wide mQTL study on 5,543 CSF metabolite levels using data from 977 subjects (age 52.7±16.6 years, 63.8%female) from the Amsterdam Dementia Cohort (n = 487; 220 controls, 87 mild cognitive impairment, 180 AD‐type dementia) and the Utrecht cohort (n = 490 controls). We measured CSF metabolites with three platforms: GC‐TOF MS (primary metabolism, 393 metabolites), CSH‐QTOF MS/MS (complex lipids, 3,532 metabolites), and HILIC‐QTOF MS/MS (biogenic amines, 1,618 metabolites). Association signals between genetic variants and CSF metabolite levels were tested using linear regression, adjusted for principal components, age, sex and site (Amsterdam/Utrecht) using a Bonferroni‐corrected genome‐wide significance threshold (5.0e−8 /754 independent CSF signals = 6.0e−11). To identify metabolites with predicted CSF levels associated with AD, we integrated metabolite summary statistics with AD summary statistics (Kunkle et al., 2019; Bellenguez et al., 2022), using a metabolome‐wide association study (MWAS) approach (FUSION software).ResultWe identified 89 genome‐wide CSF mQTLs, including 89 independent loci for 62 CSF metabolite levels. Most mQTLs were biogenic amines (81), followed by mQTLs for the primary (5) metabolism and complex lipids (3) (Figure 1). MWAS results included 24 metabolite‐AD associations with P&lt;0.05. The strongest associations included variants proximal to the CANX gene with unannotated CSF metabolite 1.09_397.32 (PBellenguez = 2.4e−6; PKunkle = 3.4e−3), and CCDC124 variants with 8.76_225.06 (PBellenguez = 2.9e−3; PKunkle = 2.8e−5) (Figure 2).ConclusionWe identified novel metabolite‐AD risk associations, including CANX and CCDC124 with unknown CSF metabolites. In future work we aim to identify these CSF metabolites using raw spectra and intensity data. This study shows that the multi‐modal integration of genetics with CSF metabolomics helps in the functional interpretation of genetic variance, and could therefore serve as useful approach for other brain‐related disorders.

Genomic atlas of human cerebrospinal fluid and brain metabolomics provides in‐depth understanding of cellular mechanism for neurodegeneration

AbstractBackgroundBrain metabolism is critical for the pathological mechanism of neurodegeneration. However, most metabolite quantitative trait loci (MQTL) studies have used tissues other than brain or cerebrospinal fluid (CSF) and are not ideal for neurodegeneration. We therefore pursued a large‐scale brain and CSF study to identify metabolites contributing to 27 brain‐related diseases.MethodWe obtained 440 metabolites in 2311 CSF samples and 962 metabolites in 1016 brain samples (WashU, ROSMAP, MAYO). We identified MQTL and performed metabolome‐wide association study (MWAS) to identify metabolites affecting diseases. Mendelian randomization (MR) and colocalization was performed for causality and shared genetic regulations.ResultWe identified 192 associations for 144 CSFmetabolites at 102 loci. Of those, 122 associations and 24 loci were novel, indicating tissue specificity. The brain study found 35 associations at 27 loci. Through MWAS, we identified 57 metabolites associated with 17 traits, in which 21 colocalized. Of the eight metabolites associated with AD, succinylcarnitine and adenine were causal. Succinylcarnitine was associated with AD risk gene LACTB, which product regulates lipid metabolism in mitochondrial, where succinylcarnitine generates energy. Both succinylcarnitine and LACTB have been implicated in obesity, a risk factor for AD. For Parkinson disease (PD), low galactosylglycerol level was causal, with signal colocalized at GALC with PD. The knockout of GALC gene prevented alpha‐synuclein accumulation in PD mice model, indicating that galactosylceramidase contributed to the development of PD. For cognitive performance, high levels of three metabolites (6‐oxopiperidine‐2‐carboxylate, 3‐hydroxyisobutyrate and argininosuccinate) were causal for low cognition, with additional support from colocalization and literature evidences. Pyridoxine‐dependent epilepsy patients, often with impaired cognition, had increased level of 6‐oxopiperidine‐2‐carboxylate. AD patients who underwent medium chain triglycerides treatment and improved cognition had changed level of beta‐hydroxybutyrate, related to 3‐hydroxyisobutyrate. Moreover, argininosuccinate lyase deficiency patients often develop severe cognitive impairment and seizures.ConclusionOur large‐scale CSF and brain MQTL study identified tissue‐specific MQTLs and multiple metabolites contributing to neurodegenerative disorders. Our results expand the knowledge on neurodegeneration, providing insights to neurodegeneration etiology, including AD, PD, and cognitive function.

Evaluating changes in firefighter urinary metabolomes after structural fires: an untargeted, high resolution approach

Firefighters have elevated rates of urinary tract cancers and other adverse health outcomes, which may be attributable to environmental occupational exposures. Untargeted metabolomics was applied to characterize this suite of environmental exposures and biological changes in response to occupational firefighting. 200 urine samples from 100 firefighters collected at baseline and two to four hours post-fire were analyzed using untargeted liquid-chromatography and high-resolution mass spectrometry. Changes in metabolite abundance after a fire were estimated with fixed effects linear regression, with false discovery rate (FDR) adjustment. Partial least squares discriminant analysis (PLS-DA) was also used, and variable important projection (VIP) scores were extracted. Systemic changes were evaluated using pathway enrichment for highly discriminating metabolites. Metabolome-wide-association-study (MWAS) identified 268 metabolites associated with firefighting activity at FDR q < 0.05. Of these, 20 were annotated with high confidence, including the amino acids taurine, proline, and betaine; the indoles kynurenic acid and indole-3-acetic acid; the known uremic toxins trimethylamine n-oxide and hippuric acid; and the hormone 7a-hydroxytestosterone. Partial least squares discriminant analysis (PLS-DA) additionally implicated choline, cortisol, and other hormones. Significant pathways included metabolism of urea cycle/amino group, alanine and aspartate, aspartate and asparagine, vitamin b3 (nicotinate and nicotinamide), and arginine and proline. Firefighters show a broad metabolic response to fires, including altered excretion of indole compounds and uremic toxins. Implicated pathways and features, particularly uremic toxins, may be important regulators of firefighter’s increased risk for urinary tract cancers.

Linking Air Pollution Exposure to Blood-Based Metabolic Features in a Community-Based Aging Cohort with and without Dementia.

Long-term exposure to air pollution has been associated with changes in levels of metabolites measured in the peripheral blood. However, most research has been conducted in ethnically homogenous, young or middle-aged populations. To study the relationship between the plasma metabolome and long-term exposure to three air pollutants: particulate matter (PM) less than 2.5μm in aerodynamic diameter (PM2.5), PM less than 10μm in aerodynamic diameter (PM10), and nitrogen dioxide (NO2) in an ethnically diverse, older population. Plasma metabolomic profiles of 107 participants of the Washington Heights and Inwood Community Aging Project in New York City, collected from 1995-2015, including non-Hispanic white, Caribbean Hispanic, and non-Hispanic Black older adults were used. We estimated the association between each metabolic feature and predicted annual mean exposure to the air pollutants using three approaches: 1) A metabolome wide association study framework; 2) Feature selection using elastic net regression; and 3) A multivariate approach using partial-least squares discriminant analysis. 79 features associated with exposure to PM2.5 but none associated with PM10 or NO2. PM2.5 exposure was associated with altered amino acid metabolism, energy production, and oxidative stress response, pathways also associated with Alzheimer's disease. Three metabolites were associated with PM2.5 exposure through all three approaches: cysteinylglycine disulfide, a diglyceride, and a dicarboxylic acid. The relationship between several features and PM2.5 exposure was modified by diet and metabolic diseases. These relationships uncover the mechanisms through which PM2.5 exposure can lead to altered metabolic outcomes in an older population.

CSF metabolites associated with biomarkers of Alzheimer's disease pathology.

Metabolomics technology facilitates studying associations between small molecules and disease processes. Correlating metabolites in cerebrospinal fluid (CSF) with Alzheimer's disease (AD) CSF biomarkers may elucidate additional changes that are associated with early AD pathology and enhance our knowledge of the disease. The relative abundance of untargeted metabolites was assessed in 161 individuals from the Wisconsin Registry for Alzheimer's Prevention. A metabolome-wide association study (MWAS) was conducted between 269 CSF metabolites and protein biomarkers reflecting brain amyloidosis, tau pathology, neuronal and synaptic degeneration, and astrocyte or microglial activation and neuroinflammation. Linear mixed-effects regression analyses were performed with random intercepts for sample relatedness and repeated measurements and fixed effects for age, sex, and years of education. The metabolome-wide significance was determined by a false discovery rate threshold of 0.05. The significant metabolites were replicated in 154 independent individuals from then Wisconsin Alzheimer's Disease Research Center. Mendelian randomization was performed using genome-wide significant single nucleotide polymorphisms from a CSF metabolites genome-wide association study. Metabolome-wide association study results showed several significantly associated metabolites for all the biomarkers except Aβ42/40 and IL-6. Genetic variants associated with metabolites and Mendelian randomization analysis provided evidence for a causal association of metabolites for soluble triggering receptor expressed on myeloid cells 2 (sTREM2), amyloid β (Aβ40), α-synuclein, total tau, phosphorylated tau, and neurogranin, for example, palmitoyl sphingomyelin (d18:1/16:0) for sTREM2, and erythritol for Aβ40 and α-synuclein. This study provides evidence that CSF metabolites are associated with AD-related pathology, and many of these associations may be causal.

Associations between Metabolomic Biomarkers of Avocado Intake and Glycemia in the Multi-Ethnic Study of Atherosclerosis

BackgroundAvocado consumption is linked to better glucose homeostasis, but small associations suggest potential population heterogeneity. Metabolomic data capture the effects of food intake after digestion and metabolism, thus accounting for individual differences in these processes. ObjectivesTo identify metabolomic biomarkers of avocado intake and to examine their associations with glycemia. MethodsBaseline data from 6224 multi-ethnic older adults (62% female) included self-reported avocado intake, fasting glucose and insulin, and untargeted plasma proton nuclear magnetic resonance metabolomic features (metabolomic data were available for a randomly selected subset; N = 3438). Subsequently, incident type 2 diabetes (T2D) was assessed over an ∼18 y follow-up period. A metabolome-wide association study of avocado consumption status (consumer compared with nonconsumer) was conducted, and the relationship of these features with glycemia via cross-sectional associations with fasting insulin and glucose and longitudinal associations with incident T2D was examined. ResultsThree highly-correlated spectral features were associated with avocado intake at metabolome-wide significance levels (P < 5.3 ∗ 10–7) and combined into a single biomarker. We did not find evidence that these features were additionally associated with overall dietary quality, nor with any of 47 other food groups (all P > 0.001), supporting their suitability as a biomarker of avocado intake. Avocado intake showed a modest association only with lower fasting insulin (β = –0.07 +/- 0.03, P = 0.03), an association that was attenuated to nonsignificance when additionally controlling for body mass index (kg/m2). However, our biomarker of avocado intake was strongly associated with lower fasting glucose (β = –0.22 +/- 0.02, P < 2.0 ∗ 10–16), lower fasting insulin (β = –0.17 +/- 0.02, P < 2.0 ∗ 10–16), and a lower incidence of T2D (hazard ratio: 0.68; 0.63–074, P < 2.0 ∗ 10–16), even when adjusting for BMI. ConclusionsHighly significant associations between glycemia and avocado-related metabolomic features, which serve as biomarkers of the physiological impact of dietary intake after digestion and absorption, compared to modest relationships between glycemia and avocado consumption, highlights the importance of considering individual differences in metabolism when considering diet-health relationships.

Renoprotective effects of genetically proxied fibroblast growth factor 21: Mendelian randomization, proteome-wide and metabolome-wide association study

BackgroundFibroblast growth factor 21 (FGF21) has demonstrated efficacy for reducing liver fat and reversing non-alcoholic steatohepatitis in phase 2 clinical trials. It is also postulated to have anti-fibrotic effects and therefore may be amenable to repurposing for the prevention and treatment of chronic kidney disease (CKD). MethodsWe leverage a missense genetic variant, rs739320 in the FGF21 gene, that associates with magnetic resonance imaging-derived liver fat as a clinically validated and biologically plausible instrumental variable for studying the effects of FGF21 analogs. Performing Mendelian randomization, we ascertain associations between instrumented FGF21 and kidney phenotypes, cardiometabolic disease risk factors, as well as the circulating proteome (Somalogic, 4907 aptamers) and metabolome (Nightingale platform, 249 metabolites). ResultsWe report consistent renoprotective associations of genetically proxied FGF21 effect, including higher glomerular filtration rates (p = 1.9 × 10−4), higher urinary sodium excretion (p = 5.1 × 10−11), and lower urine albumin-creatinine ratio (p = 3.6 × 10−5). These favorable effects translated to lower CKD risk (odds ratio per rs739320 C-allele, 0.96; 95%CI, 0.94–0.98; p = 3.2 × 10−4). Genetically proxied FGF21 effect was also associated with lower fasting insulin, waist-to-hip ratio, blood pressure (systolic and diastolic BP, p < 1.0 × 10−07) and blood lipid (low-density lipoprotein cholesterol, triglycerides and apolipoprotein B, p < 6.5 × 10−24) profiles. The latter associations are replicated in our metabolome-wide association study. Proteomic perturbations associated with genetically predicted FGF21 effect were consistent with fibrosis reduction. ConclusionThis study highlights the pleiotropic effects of genetically proxied FGF21 and supports a re-purposing opportunity for the treatment and prevention of kidney disease specifically. Further work is required to triangulate these findings, towards possible clinical development of FGF21 towards the treatment and prevention of kidney disease.