Metabolite Data Research Articles

Machine Learning Models Decoding the Association Between Urinary Stone Diseases and Metabolic Urinary Profiles

Background: Employing advanced machine learning models, we aim to identify biomarkers for urolithiasis from 24-h metabolic urinary abnormalities and study their associations with urinary stone diseases. Methods: We retrospectively recruited 468 patients at Peking Union Medical College Hospital who were diagnosed with urinary stone disease, including renal, ureteral, and multiple location stones, and had undergone a 24-h urine metabolic evaluation. We applied machine learning methods to identify biomarkers of urolithiasis from the urinary metabolite profiles. In total, 148 (34.02%) patients were with kidney stones, 34 (7.82%) with ureter stones, and 163 (34.83%) with multiple location stones, all of whom had detailed urinary metabolite data. Our analyses revealed that the Random Forest algorithm exhibited the highest predictive accuracy, with AUC values of 0.809 for kidney stones, 0.99 for ureter stones, and 0.775 for multiple location stones. The Super Learner Ensemble Method also demonstrated high predictive performance with slightly lower AUC values compared to Random Forest. Further analysis using multivariate logistic regression identified significant features for each stone type based on the Random Forest method. Results: We found that 24-h urinary magnesium was positively associated with both kidney stones and multiple location stones (OR = 1.195 [1.06–1.3525] and 1.3258 [1.1814–1.4949]) due to its high correlation with urinary phosphorus, while 24-h urinary creatinine was a protective factor for kidney stones and ureter stones, with ORs of 0.9533 [0.9117–0.996] and 0.8572 [0.8182–0.8959]. eGFR was a risk factor for ureter stones and multiple location stones, with ORs of 1.0145 [1.0084–1.0209] and 1.0148 [1.0077–1.0223]. Conclusion: Machine learning techniques show promise in revealing the links between urological stone disease and 24-h urinary metabolic data. Enhancing the prediction accuracy of these models leads to improved dietary or pharmacological prevention strategies.

The role of 1400 plasma metabolites in gastric cancer: A bidirectional Mendelian randomization study and metabolic pathway analysis.

While observational studies have illustrated correlations between plasma metabolites and gastric cancer (GC), the causal association between the 2 is still unclear. Our study aims to delineate the bidirectional relationship between plasma metabolites and GC and find potential metabolic pathways. We undertook a bidirectional 2-sample Mendelian randomization (MR) analysis to investigate the causal relationship, specificity, and direction of association between 1400 plasma metabolites and GC. The GWAS data for metabolites was obtained from a cohort of 8299 European individuals. And the GC's GWAS data was from FinnGen Consortium with 2384 European individuals, and the GWAS catalog with 1029 European ancestry cases for validation. Causal estimates were primarily calculated by the inverse-variance weighted (IVW) method. To ensure robustness, we performed comprehensive sensitivity analyses to assess heterogeneity and address concerns regarding horizontal pleiotropy. We validated the forward relationship between metabolites and GC from another database and implemented meta-analysis. Furthermore, we conducted metabolic enrichment and pathway analysis of these causal metabolites using MetaboAnalyst5.0/6.0 with the database of Kyoto Encyclopedia of Genes and Genomes. All statistical analysis was carried out using R software. Metabolites like 2s, 3R-dihydroxybutyrate, 4-acetamidobutanoate, ferulic acid 4-sulfate and methyl indole-3-acetate was proven positively linked with the development of GC. Asparagine, glucose to maltose ratio, glycohyocholate, Gulonate levels, linoleoyl ethanolamide and Spermidine to (N(1) + N(8))-acetylspermidine ratio was proven to be negatively associated with GC. Moreover, linoleic acid, histidine, glutamine, bilirubin, Succinate to proline ratio were found to be potentially linked to the development of GC. Furthermore, our analysis identified 18 significant metabolic pathways, including Arginine and proline metabolism (P < .009) and Valine, leucine, and isoleucine biosynthesis (P < .031). Our findings offer evidence supporting potential casual relations between multiple plasma metabolites and GC. These findings may offer great potential for future application of these biomarkers in GC screening and clinical prevention strategies.

Revealing the Immune Response of Sitona callosus Gyllenhal to Entomopathogenic Fungi Beauveria bassiana Infection Through Integrative Analyses of Transcriptomics and Metabolomics

In this study, we selected Sitona callosus, one of the primary insect pests of alfalfa, as the experimental insect and infected it with Beauveria bassiana. Transcriptomic and metabolomic analyses were conducted to explore alterations in gene expression and metabolic processes in S. callosus at 48, 96, and 144 h post infection with B. bassiana. The transcriptomic analysis unveiled that B. bassiana infection boosted immune responses in tubercula, affecting carbohydrate metabolism, cytochrome P450 activity, lysosome function, apoptosis regulation, phagosome formation, glutathione metabolism, amino acid metabolism, and pathogen response pathways. Subsequent metabolomics analysis confirmed that glycerophospholipids, carboxylic acids and derivatives, organooxygen compounds, keto acids and derivatives, and azane immune metabolites were significantly upregulated in response to B. bassiana infection. Additionally, we utilized the Pearson correlation coefficient method to examine the relationships between differentially expressed immune-related genes and metabolites, revealing notably strong correlations between these two sets of variables. By leveraging the WGCNA method to analyze immune metabolite data for immune-related genes, we identified hub genes crucial at various stages of immune activation. These central genes predominantly included C-type lectin receptors for pattern recognition, cytochrome P450 enzymes linked to detoxification processes, and cathepsin proteases. By combining transcriptome and metabolome analyses, it was determined that autophagy and arachidonic acid metabolism play significant roles in the response of S. callosus to infection by B. bassiana. This research will facilitate the understanding of the immune response to B. bassiana infection in adult S. callosus, laying a theoretical groundwork for future biological control strategies targeting S. callosus.

Metabolomics and dual proteomics identify contrasting patterns of major pathways affected in asparagus shoot upon Fusarium infection

Abstract Aims Infections with soil-borne pathogens have considerable detrimental effects on asparagus (Asparagus officinalis) growth and production, notably caused by the Fusarium species F. oxysporum f.sp. asparagi, F. proliferatum, and F. redolens. To get insight into the systemic effects of fungal infection on plant physiology to identify candidate resistance traits, we investigated this interaction using a multi omics approach. Methods Asparagus plants were inoculated with one of the three Fusarium species. After 8 weeks, basal stem parts were harvested and subjected to metabolome and proteome analysis as well as detection of fungal DNA. Results Upon infection, the pathogen spreads systemically from the root to the shoot and, consequently, fungal DNA and mycotoxins were detected in the basal part of the plant stem. Metabolite data revealed that the main pathway affected by Fusarium infections was “Fatty acids”, specifically the superclasses “Glycerophospholipids”, “Glycerolipids” and “Sphingolipids” being lower abundant upon infection. Another main pathway identified in the analysis was “Shikimates and Phenylpropanoids” with compounds assigned to these classes being mainly enriched upon infection. Proteome data revealed an induction of pathogen-defense proteins upon infection in asparagus, while proteins involved in vesicle trafficking and lipid metabolism were lower abundant. Conclusions This indicates that not only lipid-based signaling processes are distorted by Fusarium, but also fundamental processes such as vesicle formation, membrane integrity and cell wall organization. In planta proteome analysis of F. oxysporum led to the identification of 1,488 fungal proteins, including proteins involved in metabolic and cellular processes as well as putative virulence factors.

Population Pharmacokinetic Model of Vitamin D3 and Metabolites in Chronic Kidney Disease Patients with Vitamin D Insufficiency and Deficiency.

Vitamin D insufficiency and deficiency are highly prevalent in patients with chronic kidney disease (CKD), and their pharmacokinetics are not well described. The primary study objective was to develop a population pharmacokinetic model of oral cholecalciferol (VitD3) and its three major metabolites, 25-hydroxyvitamin D3 (25D3), 1,25-dihydroxyvitamin D3 (1,25D3), and 24,25-dihydroxyvitamin D3 (24,25D3), in CKD patients with vitamin D insufficiency and deficiency. CKD subjects (n = 29) were administered one dose of oral VitD3 (5000 I.U.), and nonlinear mixed effects modeling was used to describe the pharmacokinetics of VitD3 and its metabolites. The simultaneous fit of a two-compartment model for VitD3 and a one-compartment model for each metabolite represented the observed data. A proportional error model explained the residual variability for each compound. No assessed covariate significantly affected the pharmacokinetics of VitD3 and metabolites. Visual predictive plots demonstrated the adequate fit of the pharmacokinetic data of VitD3 and metabolites. This is the first reported population pharmacokinetic modeling of VitD3 and metabolites and has the potential to inform targeted dose individualization strategies for therapy in the CKD population. Based on the simulation, doses of 600 International Unit (I.U.)/day to 1000 I.U./day for 6 months are recommended to obtain the target 25D3 concentration of between 30 and 60 ng/mL. These simulation findings could potentially contribute to the development of personalized dosage regimens for vitamin D treatment in patients with CKD.

Identification of novel hypertension biomarkers using explainable AI and metabolomics

BackgroundThe global incidence of hypertension, a condition of elevated blood pressure, is rising alarmingly. According to the World Health Organization’s Qatar Hypertension Profile for 2023, around 33% of adults are affected by hypertension. This is a significant public health concern that can lead to serious health complications if left untreated. Metabolic dysfunction is a primary cause of hypertension. By studying key biomarkers, we can discover new treatments to improve the lives of those with high blood pressure.AimsThis study aims to use explainable artificial intelligence (XAI) to interpret novel metabolite biosignatures linked to hypertension in Qatari Population.MethodsThe study utilized liquid chromatography-mass spectrometry (LC/MS) method to profile metabolites from biosamples of Qatari nationals diagnosed with stage 1 hypertension (n = 224) and controls (n = 554). Metabolon platform was used for the annotation of raw metabolite data generated during the process. A comprehensive series of analytical procedures, including data trimming, imputation, undersampling, feature selection, and biomarker discovery through explainable AI (XAI) models, were meticulously executed to ensure the accuracy and reliability of the results.ResultsElevated Vanillylmandelic acid (VMA) levels are markedly associated with stage 1 hypertension compared to controls. Glycerophosphorylcholine (GPC), N-Stearoylsphingosine (d18:1/18:0)*, and glycine are critical metabolites for accurate hypertension prediction. The light gradient boosting model yielded superior results, underscoring the potential of our research in enhancing hypertension diagnosis and treatment. The model’s classification metrics: accuracy (78.13%), precision (78.13%), recall (78.13%), F1-score (78.13%), and AUROC (83.88%) affirm its efficacy. SHapley Additive exPlanations (SHAP) further elucidate the metabolite markers, providing a deeper understanding of the disease’s pathology.ConclusionThis study identified novel metabolite biomarkers for precise hypertension diagnosis using XAI, enhancing early detection and intervention in the Qatari population.

Systematic evaluation of the correlation between serum metabolites and tinnitus

Objective: We performed a Mendelian randomisation (MR) analysis to explore the relationship between serum metabolites and tinnitus. Methods: In this study, 486 serum metabolites were considered as exposure factors, and single nucleotide polymorphisms (SNP) significantly associated with them were used as instrumental variables (IV). The serum metabolite data were obtained from a public database (http://metabolomips.org/gwas/index.php), while the genome-wide association study (GWAS) summary association statistics for tinnitus were obtained from a Finnish database (https://r10.finngen.fi/pheno/H8_TINNITUS). The inverse variance weighting (IVW) method was employed as the primary determination method for MR analysis, with corrections for multiple comparisons made using the false discovery rate (FDR). Sensitivity tests were conducted using the MR-Egger regression, Mendelian random polymorphism residuals and outliers (MR-PRESSO) methods. The identified serum metabolites were subjected to chained disequilibrium regression analysis (LDSC) and metabolic pathway analysis. Reverse MR analysis was performed to investigate the possibility of reverse causality. Analyses were performed in R software (version 4.3.1). Results: A total of 17 serum metabolites (including 10 known and 7 unknown metabolites) associated with tinnitus were identified. The known metabolites included protective metabolites such as acetylcarnitine, hydroxyisovaleryl carnitine, glycine, monounsaturated glycerol ester, and glycine-L-valine, and hazardous metabolites such as allantoin, glycerylphosphorylcholine 1-eicosatrienoate, myo-inositol, 15-methylpalmitate, and pseudouridine; the strongest causally protective metabolites were acetylcarnitine, the followed by hydroxyisopentanoyl carnitine and glycine; the hazardous metabolite with the strongest causal effect was pseudouridine, followed by inositol and 15-methylpalmitate; and only hydroxyisopentanoyl carnitine (PFDR=0.04) and glycerol monooleate (PFDR=0.04) reached significance values after FDR correction. The findings were free of heterogeneity, pleiotropy and reverse causal associations. The metabolic pathways were mainly enriched in pathways such as ascorbic acid and aldolac metabolism. Conclusions: The study suggests a causal relationship between serum metabolites and tinnitus risk. Serum metabolite levels may influence tinnitus-related metabolic pathways.

6828 Exploring Metabolomic Clues in Diabetic Retinopathy

Abstract Disclosure: M.W. Simonson: None. Y. Li: None. J.J. McAnany: None. B. Prasad: None. E.C. Hanlon: None. S. Pannain: None. B.T. Layden: None. E. Van Cauter: None. J.C. Park: None. S.J. Crowley: None. F.Y. Chau: None. K.K. Danielson: None. H. Chen: None. G.E. Chlipala: None. C. Martinez: None. S. Reutrakul: Speaker; Self; Eli Lilly &amp; Company. Background: Diabetic retinopathy (DR) is a common microvascular complication of diabetes. Metabolomics offers the possibility for novel biomarker discovery. The purpose of this study was to determine metabolomic differences that characterized patients with versus without DR. Methods: A total of 62 serum samples were collected from 36 type 2 diabetes (T2D) patients with DR and 26 T2D patients without DR, and analyzed via UPLC-MS. The metabolite data were normalized using median normalization. The differential expression of metabolites was compared among the groups using the limma package in R. The covariates (age, sex, BMI and sleep apnea severity) were accounted for by using a generalized linear model fitted to the data. Significantly different metabolites were filtered by an absolute log2 Fold Change (FC) of 1.5 or greater and Q values &amp;lt; 0.05. Results: Mean (SD) age for DR and no-DR group was 55 (±5.9) versus 54 (±6.7) years, while BMI was 33 (±5.8) versus 33 (±6.3) kg/m2 respectively. Both DR and no-DR groups were 58% female. A1C levels in DR group was marginally higher than in no-DR group (p=0.09). There were significant differences in metabolomic changes between DR versus no-DR, with 166 total differential metabolites after adjusting for covariates and filtering by FC. Notable metabolites included those involved in fatty acid metabolism, acyl carnitines, prostaglandins, and phospholipids. There was an overall upregulation of serum acyl carnitines in DR as compared to no-DR, including 2,6 dimethylheptanoyl carnitine (FC = 3.0) and cis-5-Tetradecenoylcarnitine (FC = 2.0). Additionally, there was a decrease in long chain fatty acids in DR as compared to no-DR, which included 6-hydroxypentadecanedioic acid (FC = -6.6), 10,16-dihydroxy-palmitic acid (FC = -6.0), and tetracosatetraenoyl CoA (FC = -6.1). The 11-beta prostaglandin F2 (11-PGF2) was increased in DR (FC = 1.9). Circulating phospholipids including lysophosphatidylcholine (22:2) (lysoPC) (FC = 1.8) and phosphatidylcholine (24:1/24:1) (PC) (FC = 5.9) were also significantly dysregulated. Conclusions: We identified a subset of metabolites that discriminate T2D patients with or without DR. The changes in acyl carnitines and long chain fatty acids may reflect mitochondrial dysfunction in DR. The presence of certain prostaglandins highlights the inflammatory environment which may facilitate DR progression. Increases in phospholipids such as lysoPC suggest an atherogenic state and higher LDL oxidation, while increases in PC may indicate altered lipogenesis in DR. These findings may serve as new potential serum biomarkers to promote efficient preventive care and provide mechanistic insight into understanding DR pathophysiology. Presentation: 6/2/2024

Investigating the relationship between blood metabolites and diabetic retinopathy using two-sample mendelian randomization and in vivo validation

We addressed fundamental questions about the influence of metabolites on the development of Diabetic retinopathy (DR), and explored the related pathological mechanism. Genome-wide association study (GWAS) database data for metabolites and DR were used to perform Mendelian randomization (MR) studies. The inverse variance weighting (IVW) was chosen as the primary analysis method. Sensitivity analysis was conducted using MR-PRESSO, leave-one-out and Cochran’s Q test. Confounding factors were eliminated to ensure robustness. We also conducted metabolic pathway analysis. In vivo experimental validation was conducted using Sprague Dawley rats. The serum metabolites of the DR group rats and normal group rats were examined to evaluate the MR results. The screen identified eighteen metabolites associated with DR risk, twelve of which were known components. Seven metabolites were positively correlated with DR risk, while five could reduce it. Eight metabolites associated with proliferative DR (PDR) risk were identified, four of which are known components. Three of these were positively associated with PDR risk and one metabolite reduced PDR risk. Additionally, two possible metabolic pathways involved in the biological mechanism of DR were identified. The ELISA results showed that the serum levels of isoleucine and 4-HPA were significantly increased in DR rats, while the level of inosine was decreased. This study offers novel insights into the biological mechanisms underlying DR. Metabolites that are causally linked to DR may serve as promising biomarkers and therapeutic targets.

DMRdb: a disease-centric Mendelian randomization database for systematically assessing causal relationships of diseases with genes, proteins, CpG sites, metabolites and other diseases.

Exploring the causal relationships of diseases with genes, proteins, CpG sites, metabolites and other diseases is fundamental to the life sciences. However, large-scale research using Mendelian randomization (MR) analysis is currently lacking. To address this, we introduce DMRdb (http://www.inbirg.com/DMRdb/), a disease-centric Mendelian randomization database, designed to systematically assess causal relationships of diseases with genes, proteins, CpG sites, metabolites and other diseases. The database consists of three main components: (i) 6640 high-quality disease genome-wide association studies (GWASs) from public sources that were subjected to rigorous quality filtering and standardization; (ii) over 497 billion results from MR analyses involving 6640 disease GWAS datasets, 16 238 expression quantitative trait loci (eQTLs) data, 2564 protein quantitative trait loci (pQTLs) data, 12 000 methylation quantitative trait locus (meQTLs) data and 825 metabolites data and (iii) over 380 000 causal relationship pairs from 1223 literature sources relevant to MR analyses. A user-friendly online database was developed to allow users to query, search, and download all the results. In summary, we anticipate that DMRdb will be a valuable resource for advancing our understanding of disease mechanisms and identifying new biomarkers and therapeutic targets.

Parkinson's disease is characterized by vitamin B6-dependent inflammatory kynurenine pathway dysfunction.

Parkinson's disease (PD) is a complex multisystem disorder clinically characterized by motor, non-motor, and premotor manifestations. Pathologically, PD involves neuronal loss in the substantia nigra, striatal dopamine deficiency, and accumulation of intracellular inclusions containing aggregates of α-synuclein. Recent studies demonstrate that PD is associated with dysregulated metabolic flux through the kynurenine pathway (KP), in which tryptophan is converted to kynurenine (KYN), and KYN is subsequently metabolized to neuroactive compounds quinolinic acid (QA) and kynurenic acid (KA). This multicenter study used highly sensitive liquid chromatography-tandem mass-spectrometry to compare blood and cerebral spinal fluid (CSF) KP metabolites between 158 unimpaired older adults and 177 participants with PD. Results indicate that increased neuroexcitatory QA/KA ratio in both plasma and CSF of PD participants associated with peripheral and cerebral inflammation and vitamin B6 deficiency. Furthermore, increased QA tracked with CSF tau and severity of both motor and non-motor PD clinical dysfunction. Importantly, plasma and CSF kynurenine metabolites classified PD participants with a high degree of accuracy (AUC = 0.897). Finally, analysis of metabolite data revealed subgroups with distinct KP profiles, and these were subsequently found to display distinct PD clinical features. Together, these data further support the hypothesis that the KP serves as a site of brain and periphery crosstalk, integrating B-vitamin status, inflammation and metabolism to ultimately influence PD clinical manifestation.

Genetically predicted blood metabolites mediate relationships between gut microbiota and ovarian cancer: a Mendelian randomization study.

While there is evidence that gut microbiota (GM) and blood metabolites are associated with ovarian cancer (OC), the precise mechanisms underlying this relationship are still unclear. This study used Mendelian randomization (MR) to elucidate the causal connections between GM, blood metabolite biomarkers, and OC. In this study, we leveraged summary data for GM (5,959 individuals with genotype-matched GM), blood metabolites (233 circulating metabolic traits with 136,016 participants), and OC (63,702 participants with 23,564 cases and 40,138 controls) from genome-wide association studies (GWASs). We performed MR analysis to explore the causal relationship between GM and OC. Further, we harnessed univariable MR (UVMR) analysis to evaluate the causal associations between GM and circulating metabolites. Finally, we employed a two-step approach based on multivariable MR (MVMR) to evaluate the total genetic prediction effect of metabolites mediating the GM on the risk of OC to discover a potential causal relationship. In the MR analysis, 24 gut bacteria were causally associated with the pathogenesis of OC, including 10 gut bacteria (Dorea phocaeense, Succinivibrionaceae, Raoultella, Phascolarctobacterium sp003150755, Paenibacillus J, NK4A144, K10, UCG-010 sp003150215, Pseudomonas aeruginosa, and Planococcaceae) that were risk factors, and 14 gut bacteria (CAG-177 sp002438685, GCA-900066135 sp900066135, Enorma massiliensis, Odoribacter laneus, Ruminococcus E sp003521625, Streptococcus sanguinis, Turicibacter sp001543345, Bacillus velezensis, CAG-977, CyanobacteriaStaphylococcus A fleurettii, Caloranaerobacteraceae, RUG472 sp900319345, and CAG-269 sp001915995) that were protective factors. The UVMR analysis showed that these 24 positive gut bacteria were causally related to lipoproteins, lipids, and amino acids. According to the MVMR analysis, Enorma massiliensis could reduce the risk of OC by raising the total cholesterol to total lipids ratio in large low-density lipoprotein (LDL) and cholesteryl esters to total lipids ratio in intermediate-density lipoprotein (IDL). Turicibacter sp001543345, however, could reduce the risk of OC by lowering free cholesterol in small high-density lipoprotein (HDL) and increasing the ratios of saturated fatty acids to total fatty acids, total cholesterol to total lipids ratio in very small very-low-density lipoprotein (VLDL), and cholesteryl esters to total lipids ratio in very small VLDL. The current MR study provides evidence that genetically predicted blood metabolites can mediate relationships between GM and OC.

356 Exploring the manganese requirement of feedlot steers

Abstract Angus-cross steers [n = 144; body weight (BW) = 359 kg ± 13.4] were used to assess the effect of dietary Mn and steroidal implants on trace mineral (TM) status, and liver and serum metabolites related to N metabolism. Steers (n = 6/pen) were stratified by BW in a 3 × 2 factorial. GrowSafe bunks recorded individual feed intake (experimental unit = steer; n = 24/treatment). Dietary treatments included (MANG; 8 pens per treatment; Mn as MnSO4): 1) no supplemental Mn (analyzed 14 mg Mn/kg DM; Mn0); 2) 20 mg supplemental Mn/kg DM (Mn20); 3) 50 mg supplemental Mn/kg DM (Mn50). Within MANG, steers received a steroidal implant treatment (IMP) on d 0: 1) no implant; NO; or 2) combination implant (Revalor-200; REV). Liver biopsies for TM analysis and qPCR, and blood for serum urea-N (SUN) analysis were collected on d 0, 20, 40, and 77. Liver TM, serum metabolite, enzyme activity, and gene expression data were analyzed as repeated measures using PROC MIXED of SAS 9.4. The Correlation Procedure of SAS 9.4 was utilized to assess correlations between liver arginase activity, SUN concentrations, and liver Mn concentrations. No MANG × IMP effects were noted (P ≥ 0.12) for growth or carcass measures. Steroidal implants increased final BW (FBW), overall average daily gain (ADG), and gain to feed (G:F; P ≤ 0.01). Dietary Mn did not influence FBW, overall ADG, or overall G:F (P ≥ 0.14). Liver Mn concentration increased with supplemental Mn (P = 0.01). An IMP × DAY effect was noted for liver Mn (P = 0.01) where NO and REV were similar on d 0 but NO cattle increased liver Mn from d 0 to 20 while REV liver Mn decreased. Relative expression of liver Mn transporter ZnT10 tended to be least (MANG; P = 0.10) in Mn20 while Mn0 and Mn100 were similar. Relative expression of liver Mn transporter ZIP8 was not influenced by MANG, IMP, or MANG × IMP (P ≥ 0.23). Liver arginase activity was not influenced by MANG, IMP, MANG × DAY, IMP × DAY, or MANG × IMP (P ≥ 0.15) but tended to decrease over time (DAY; P = 0.10). On d 0, 20, and 40, arginase activity was correlated (r ≤ 0.33; P ≤ 0.01) with SUN and liver Mn. Relative expression of MnSOD in the liver was greater in REV (P = 0.03) compared with NO and within a MANG × IMP effect (P = 0.01) REV increased liver MnSOD activity. This study indicates current NASEM Mn recommendations are adequate to meet demands of finishing beef cattle given a steroidal implant. This study also reveals how implants and supplemental Mn influence liver enzyme activity and genes related to arginine metabolism, urea synthesis, antioxidant capacity, and TM homeostasis.

Choline Metabolites and 15-Year Risk of Incident Diabetes in a Prospective Cohort of Adults: Coronary Artery Risk Development in Young Adults (CARDIA) Study.

The potential for choline metabolism to influence the development of diabetes has received increased attention. Previous studies on circulating choline metabolites and incident diabetes have been conducted in samples of older adults, often with a high prevalence of risk factors. Participants were from year 15 of follow-up (2000-2001) in the Coronary Artery Risk Development in Young Adults (CARDIA) Study (n = 3,133, aged 33-45 years) with plasma choline metabolite (choline, betaine, and trimethylamine N-oxide [TMAO]) data. We quantified associations between choline metabolites and 15-year risk of incident diabetes (n = 387) among participants free of diabetes at baseline using Cox proportional hazards regression models adjusted for sociodemographics, health behaviors, and clinical variables. Betaine was inversely associated with 15-year risk of incident diabetes (hazard ratio 0.76 [95% CI 0.67, 0.88] per 1-SD unit betaine), and TMAO was positively associated with 15-year risk of incident diabetes (1.11 [1.01, 1.22] per 1-SD unit). Choline was not significantly associated with 15-year risk of incident diabetes (1.05 [0.94, 1.16] per 1-SD). Our findings are consistent with other published literature supporting a role for choline metabolism in diabetes. Our study extends the current literature by analyzing a racially diverse population-based cohort of early middle-aged individuals in whom preventive activities may be most relevant.

The causal relationship between CSF metabolites and GBM: a two-sample mendelian randomization analysis

BackgroundGlioblastoma multiforme (GBM) is a highly aggressive primary malignant brain tumor characterized by rapid progression, poor prognosis, and high mortality rates. Understanding the relationship between cerebrospinal fluid (CSF) metabolites and GBM is crucial for identifying potential biomarkers and pathways involved in the pathogenesis of this devastating disease.MethodsIn this study, Mendelian randomization (MR) analysis was employed to investigate the causal relationship between 338 CSF metabolites and GBM. The data for metabolites were obtained from a genome-wide association study summary dataset based on 291 individuals, and the GBM data was derived from FinnGen included 91 cases and 174,006 controls of European descent. The Inverse Variance Weighted method was utilized to estimate the causal effects. Supplementary comprehensive assessments of causal effects between CSF metabolites and GBM were conducted using MR-Egger regression, Weighted Median, Simple Mode, and Weighted Mode methods. Additionally, tests for heterogeneity and pleiotropy were performed.ResultsThrough MR analysis, a total of 12 identified metabolites and 2 with unknown chemical properties were found to have a causal relationship with GBM. 1-palmitoyl-2-stearoyl-gpc (16:0/18:0), 7-alpha-hydroxy-3-oxo-4-cholestenoate, Alpha-tocopherol, Behenoyl sphingomyelin (d18:1/22:0), Cysteinylglycine, Maleate, Uracil, Valine, X-12,101, X-12,104 and Butyrate (4:0) are associated with an increased risk of GBM. N1-methylinosine, Stachydrine and Succinylcarnitine (c4-dc) are associated with decreased GBM risk.ConclusionIn conclusion, this study sheds light on the intricate interplay between CSF metabolites and GBM, offering novel perspectives on disease mechanisms and potential treatment avenues. By elucidating the role of CSF metabolites in GBM pathogenesis, this research contributes to the advancement of diagnostic capabilities and targeted therapeutic interventions for this aggressive brain tumor. Further exploration of these findings may lead to improved management strategies and better outcomes for patients with GBM.

P19-61 Combining chemical, biological similarity and metabolite data in read-across approach

P19-61 Combining chemical, biological similarity and metabolite data in read-across approach

Early clinical drug product shelf-life setting using accelerated predictive stability and metabolite data for impurity qualification: A case study

This case study demonstrates how knowledge of degradation products together with predictions can establish a lean stability strategy using the accelerated predictive stability (APS) principles. Applying all available data for AZD4831, (R)-1-(2-(1-aminoethyl)-4-chlorobenzyl)-2-thioxo-2,3-dihydro-1H-pyrrolo[3,2-d]pyrimidin-4(5H)-one, a reliable predictive model was developed despite minor differences in technical batch tablet compositions. Early forced degradation studies were performed to map potential degradation pathways. The insights from these studies guided the design of an APS study, which in turn inform on a suitable clinical stability program, initial specification and shelf-life. The use of APS predictions of degradants as well as total impurities highlighted at an early stage, when designing the clinical stability program, the opportunity to identify which degradation product that would be shelf-life limiting. Hence, it was possible to guide the development stability activities and set an initial shelf-life of a tablet formulation. The presented study displays the importance of combining several sources of information in drug development, e.g., potential degradation pathways, accelerated stability, stability program design, metabolite data, and specification limits.

Abdominal obesity and CKD: A potential mediating role of serum metabolites in the UK Biobank population

BackgroundIt is generally known that although a connection between abdominal obesity and chronic kidney disease (CKD) is well-established, there is a lack of systematic research investigating the specific roles of serum metabolites, including lipid metabolites, amino acid metabolites, carbohydrate metabolites and inflammatory substances in explaining this associations. MethodsWe included 118,020 general patients with data of serum metabolites from UK Biobank. We defined abdominal obesity and CKD based on waist circumference and ICD-10 criteria. The serum metabolites were assessed by a high-throughput nuclear magnetic resonance (NMR) based metabolic biomarker profiling platform. We conducted mediation analysis by R software and used the proportion of mediation to quantify the mediation effect. ResultsThis study demonstrated that lipid metabolites played a more important role in mediating the relationship between abdominal obesity and CKD than amino acid metabolites and carbohydrate metabolites. And Glycoprotein Acetyls (GlycA) was the strongest mediator for the correlation between abdominal obesity and CKD, accounting for 26.4 %. And In the mediation analysis stratified by sex, we found that the mediating effects of lipid metabolites were mostly higher in men than in women, while GlycA accounted for the largest proportion of the mediation association in both two groups (31.0 % for women and 19.8 % for men). ConclusionAmong lipid metabolites, amino acid metabolites, carbohydrate metabolites and inflammatory substances, our study showed that infammation marker GlycA was the novel and key mediator for the correlation between abdominal obesity and CKD.

Longitudinal characterization of the muscle metabolome in dairy cows during the transition from lactation cessation to lactation resumption

Skeletal muscle is vital in maintaining metabolic homeostasis and adapting to the physiological needs of pregnancy and lactation. Despite advancements in understanding metabolic changes in dairy cows around calving and early lactation, there are still gaps in our knowledge, especially concerning muscle metabolism and the changes associated with drying off. This study aimed to characterize the skeletal muscle metabolome in the context of the dietary and metabolic changes occurring during the transition from the cessation of lactation to the resumption of lactation in dairy cows. Twelve Holstein dairy cows housed in tie stalls were dried off 6 weeks (wk) before the expected calving date. Cows were individually fed ad libitum total mixed rations composed of grass silage, corn silage, and concentrate during lactation and of corn silage, barley straw, and concentrate during the dry period. The metabolome was characterized in skeletal muscle samples (M. longissimus dorsi) collected on wk -7 (9 d before dry-off), -5 (6 d after dry-off), and wk -1, and 1 relative to calving. The targeted metabolomics approach was conducted using the MxP Quant 500 kit (Biocrates Life Sciences AG) with liquid chromatography, flow injection, and electrospray ionization triple quadrupole mass spectrometry. Statistical analysis on the muscle metabolite data was performed using MetaboAnalyst 5.0, which allowed us to conduct various multivariate analyses such as principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), informative heat map generation, and hierarchical clustering. The statistical analysis revealed a clear separation between pregnancy (wk -7, -5, and -1) and post-calving (wk 1). Starting 5 wk before calving and continuing through the first wk thereafter, the concentration of 3-methylhistidine (3-MH) in the muscle increased. This coincided with an increase in the concentrations of 11 AA (Phe, His, Tyr, Trp, Arg, Asn, Leu, Ile, Gly, Ser, and Thr) in the first wk after calving, whereas Gln decreased. l-arginine pathway metabolites (homoarginine, ornithine, citrulline, and asymmetric dimethylarginine), betaine, and sarcosine followed a similar pattern, increasing from wk -7 to -5, but decreasing from wk -1 to 1. The transition from pregnancy to lactation was associated with an increase in concentrations of the long-chain acylcarnitine species C16, C16:1, C18, and C18:1 in the muscle, whereas the concentrations of phosphatidylcholine and sphingomyelin in the muscle remained stable. The significant changes observed in the metabolome mainly concerned the AA and AA-related metabolites, indicating muscle protein breakdown in the first wk after calving. The metabolites produced by the L-Arg pathway might contribute to regulating skeletal muscle mass and function in periparturient dairy cows. The elevated concentrations of long-chain acylcarnitine species in the muscle in the first wk after calving suggest incomplete fatty acid oxidation, likely due to insufficient metabolic adaptation in response to the fatty acid load around the time of calving.

The mediating role of metabolites between gut microbiome and Hirschsprung disease: a bidirectional two-step Mendelian randomization study.

Gut microbiome (GM) was observed to be associated with the incidence of Hirschsprung disease (HD). However, the effect and mechanism of GM in HD is still unclear. To investigate the relationship between GM and HD and the effect of metabolites as mediators, a bidirectional two-step Mendelian randomization (MR) study was conducted. The study selected instrument variables (IVs) from summary-level genome-wide association studies (GWAS). The MiBioGen consortium provided the GWAS data for GM, while the GWAS data for metabolites and HD were obtained from the GWAS Catalog consortium. Two-sample MR analyses were performed to estimate bidirectional correlations between IVs associated with GM and HD. Then, genetic variants related to 1,400 metabolite traits were selected for further mediation analyses using the Product method. This study found that seven genus bacteria had a significant causal relationship with the incidence of HD but not vice versa. 27 metabolite traits were significantly correlated with HD. After combining the significant results, three significant GM-metabolites-HD lines have been identified. In the Peptococcus-Stearoyl sphingomyelin (d18:1/18:0)-HD line, the Stearoyl sphingomyelin (d18:1/18:0) levels showed a mediation proportion of 14.5%, while in the Peptococcus-lysine-HD line, the lysine levels had a mediation proportion of 12.9%. Additionally, in the Roseburia-X-21733-HD line, the X-21733 levels played a mediation proportion of 23.5%. Our MR study indicates a protective effect of Peptococcus on HD risk that is partially mediated through serum levels of stearoyl sphingomyelin (d18:1/18:0) and lysine, and a risk effect of Roseburia on HD that is partially mediated by X-21733 levels. These findings could serve as novel biomarkers and therapeutic targets for HD.