Metabolomics

BACKGROUND: Erythrophagocytosis constitutes a major pathogenic mechanism of metabolic dysfunction associated fatty liver disease (MAFLD). Our previous research established a quantitative thin-layer chromatography (TLC) technique for sphingomyelin, revealing reduced levels in the red blood cells (erythrocytes) of patients with metabolic dysfunction associated fatty liver disease (MAFLD). This reduction was accompanied by erythrocyte sphingosine accumulation, a driver of pro-inflammatory erythrophagocytosis, though sphingosine 1-phosphate release remained stable. To better understand erythrocyte sphingosine metabolism, we adapted our quantitative TLC method to analyze sphingosine within the erythrocyte-conditioned media (ECM) of MAFLD patients. Methodology Separation was performed on 10X10cm Silica gel 60 F254 plates using a mobile phase of chloroform, methanol, acetic acid, and water (60:50:1:4 v/v/v/v). The dynamic range, linearity, and range of linearity were assessed by analysing sphingosine levels from 0.1 to 10microg/spot. We validated the system precision and sensitivity by performing triplicate analyses of sphingosine standards (1.25, 2.5, and microg). The limits of detection and quantification were derived from the calibration curve slope and standard deviation (3.3 XSD/slope for LOD; 10 XSD/slope for LOQ). Accuracy was assessed via recovery tests at 100%, 200%, and 300% of a 2.5microg load. We confirmed specificity by evaluating the retention factors against other lipid species. This protocol was applied to Folch-extracted lipids from the ECM (5 X 107 cells/ml) of four MAFLD patients and four healthy controls, spiked with 5microg of sphingosine. Findings The calibration model, based on combined Green and Blue color intensities, followed the linear equation y = -11.171x + 353.25(R2 = 0.94). Interday precision values were 0.21%, 1.65%, and 0.44%, while recovery rates (accuracy) ranged from 94.5% to 98.7%. The measured LOD and LOQ were 0.75microg and 1.21microg, respectively. The sensitivity was calculated at 90ng. Statistical analysis showed no significant variance in sphingosine concentrations in erythrocyte-conditioned media between the MAFLD group and the control group. Summary The described thin layer chromatography is accurate, precise, sensitive, with good limits of detection and quantification, and most importantly is low-cost and time-efficient. Using this method, we show that while erythrocytes of MAFLD patients exhibit sphingosine accumulation, the utilisation of exogenous sphingosine from their erythrocytes is not affected. This suggests that the metabolic shift may be driven by increased sphingosine supply from the plasma.

A multi-omic approach utilizing a single biospecimen is important to avoid intra-sample heterogeneity associated with testing multiple omic single-samples, and for more efficient use of small volumes of precious biopsies (<30 mg). This is especially true for the microanatomy of post-mortem human brain samples. Using post-mortem human brain biospecimens from the NIH NeuroBioBank, a penta-omic sequential extraction method is described, Simultaneous Metabolomic, Proteomic, Lipidomic - DNA, RNA Extraction (SiMPL-DREx). Each sequential omic extract was compared to those obtained by the gold standard single omic method. Preserving RIN is critical for brain and tissue banks, as it is a primary measure of tissue quality. For all five omic extracts, the tissue integrity numbers and omic profiles did not significantly differ from those obtained by the respective omic gold standard method. Unlike past multi-omic studies, this study quantified the relative solvent percentages and upstream losses for both the organic and aqueous phases, confirming an omics loss of under 5%.

Class imbalance remains a challenge in metabolomics research, where biological and technical variability can affect statistical inference and machine learning (ML) performance. Class-balancing algorithms address this issue by either increasing minority-class observations or reducing the number of majority-class samples. This study evaluated the impact of oversampling and undersampling algorithms on targeted and untargeted metabolomics datasets derived from LC-MS and GC-MS analyses of plasma samples from patients with glioblastoma, meningioma, and controls. Synthetic Minority Oversampling Technique (SMOTE) and Random Undersampling (RUS) were applied to balance the datasets, and their effects on data distribution, inter-feature correlations, and machine learning model performance were compared. RUS preserved the original feature distributions but reduced representativeness by removing the majority-class samples. In contrast, SMOTE introduced synthetic samples that altered covariance structures, increasing the risk of overfitting, particularly in small datasets (n=10). These effects diminished with larger groups (n=30), partially restoring correlations between metabolites. Model performance varied across the class-balancing algorithms. Random Forest classifiers benefited from both balancing methods, with undersampling often yielding higher F1 scores, whereas Support Vector Machine models showed reduced classification performance. These findings highlight the importance of selecting class-balancing strategies based on dataset size, analytical platform, and ML algorithm in metabolomics studies.

Background/ObjectivesMetabolism-level interpretation of metabolomics datasets requires aggregation analyses across metabolites. One highlyused aggregation analysis is pathway enrichment analysis (PEA), which involves detecting pathways enriched with metabolites that are differential between experimental groups. Annotating metabolites with pathway associations is a prerequisite for PEA. While several knowledgebases define pathways and include metabolite-pathway annotations, these definitions are often partially or even grossly incomplete due to limitations in current metabolic knowledge and its curation, which greatly limits the effectiveness of PEA. MethodsIn this work, we used a novel multitask classification, graph convolutional-like neural network to generate high-quality metabolite-pathway annotations for pathways defined across KEGG, MetaCyc, and Reactome. We then included these predicted metabolite-pathway annotations when performing PEA on 990 datasets deposited in Metabolomics Workbench. ResultsWe demonstrate an 8-fold increase in the median number of enriched pathways detected across these datasets compared to using only knowledgebase-derived annotations. ConclusionsThe significant increase in enriched pathways substantially improves the biological and biomedical interpretability of metabolomics datasets.

Prenatal alcohol exposure (PAE) is a major cause of neurodevelopmental disorders, yet most children are diagnosed late or misdiagnosed. Neuroplacentology suggest that placental factors released into maternal and/or umbilical cord blood contribute to fetal brain development. Consistently, a preclinical inter-organ transcriptomic database revealed that PAE disrupts the expression ratio of angiogenic and inflammatory factors suggesting an angio-inflammatory response. This study aimed i) to assay, by multiplex immunoassay, angiogenic and inflammatory factors in maternal and umbilical cord blood from alcohol-consuming women and ii) to perform a maternofetal analysis according to neonatal sex. Afterwards, dysregulated factors from mothers who gave birth to females or males were submitted to STRING and ShinyGO analyses. Results showed that PAE differently altered the distribution profiles of dysregulated angiogenic and inflammatory factors in maternal and umbilical cord blood. Moreover, sex-specific differences were observed, with 36% of dysregulated proteins specific to males, 48% to females, and 16% common to both. STRING analysis revealed robust functional protein-protein interactions linking together inflammatory and angiogenic clusters while the ShinyGO analysis identified enriched pathways related to vascular shear stress. These findings provide the first maternofetal analysis of combined angiogenic and inflammatory factors from alcohol-consuming mothers.

It was recently reported that Methylobacterium extorquens AM1 produces the citrate-hydroxamate siderophore N-deoxyschizokinen A, identified by LC-HRMS. Multiple properties were inconsistent with the assignment: the feature eluted far later than the other schizokinen derivatives (17 min versus 6-8 min), a reversed-phase shift larger than a single-hydroxyl difference in a molecule can explain, further its accurate mass deviated from the calculated one by 28 ppm, well outside the error on the co-analyzed standards and its diagnostic m/z 105 and 77 fragments suggest a molecule with an aromatic moiety. A replicate comparison of identical samples in plastic versus glass autosampler vials was decisive: the m/z 387 feature was reproducibly present with plastic vials and absent with glass. We therefore conclude that the reported detection of N-deoxyschizokinen A in M. extorquens AM1 is an artifact, and recommend glass-vial and solvent-blank controls, an explicit accurate-mass threshold, and narrow MS/MS isolation when assigning trace siderophore-like features from complex extracts.

Background: The Dietary Inflammatory Index (DII) is widely used to assess the inflammatory potential of diet, but it relies on self-reported dietary assessment and does not directly capture individual differences in metabolism as an intermediate connection to inflammation. High-resolution metabolomics provides objective measurements that complement dietary assessment to support precision nutrition to control inflammation. Objective: We developed, tested, and applied a Dietary Metabolite Inflammatory Index (DMII) to assess diet-related chronic inflammation using metabolites measured by liquid chromatography high-resolution mass spectrometry. Methods: DII was calculated using dietaryindex R package with Block Food Frequency Questionnaire (FFQ) data. To develop the DMII, chronic inflammation-related dietary metabolites corresponding to the DII food parameters were found through a literature review. Dietary metabolites were identified and quantified by authentic standards by our established laboratory procedures. DMII uses the same inflammatory effect scores as the DII. Three DMII versions were developed: concentration-based, median-based, and quintile-based DMII. Mean and standard deviation of 29 dietary metabolites were calculated by using 3025 human plasma samples from 3 studies. DMII was tested in the Center for Health Discovery and Well-Being cohort (CHDWB) and the Atlanta African American Maternal and Child cohort (ATLAA) using chronic inflammation biomarkers, including high-sensitivity C-reactive protein (hsCRP), CRP, and IL6. The median-based DMII was further applied to four Alzheimers disease metabolomics datasets as a proof-of-concept application. Results: In the CHDWB study, concentration-based DMII had a weak positive correlation with Block FFQ-derived DII and strongly correlated with median-based and quintile-based DMII. In the same study, all three DMII versions had significant positive correlations with hsCRP and IL6. In the ATLAA study, only concentration-based DMII was positively associated with CRP and IL6. Higher median-based DMII was associated with higher odds of Alzheimers disease. Conclusions: DMII provides a metabolomics-based framework for assessing diet-related chronic inflammation using metabolomics data. This metabolomics approach may complement self-reported dietary assessment to use diet and nutrition to help protect against chronic disease linked to inflammation.

BACKGROUND: Perfluorooctane sulfonate (PFOS) is a widely distributed persistent organic pollutant in the environment and has been associated with an increased risk of atherosclerosis. However, the underlying pathogenic mechanisms remain largely unclear. This study aimed to investigate the effects of PFOS on atherosclerosis and its associated gut-vascular axis. METHODS: Pseudo-germ-free mouse models and fecal microbiota transplantation (FMT) were used to determine the role of the gut microbiota in PFOS-induced atherosclerosis. Metagenomic sequencing was performed to characterize alterations in gut microbial composition following PFOS exposure, and targeted metabolomics was used to assess bile acid profiles in the ileum and plasma. Transcriptomic analysis of Bacteroides caecimuris (B.caecimuris) was conducted to explore the reasons for the increased abundance of B.caecimuris after PFOS exposure. In addition, intestinal transcriptomics and ChIP-qPCR were performed to validate transcriptional regulation within the FXR-TLR3 signaling axis. RESULTS: Among 127 participants with paired serum and fecal samples, including 82 patients undergoing coronary angiography with Gensini scores (GS score), fecal PFOS levels were significantly associated with lipid profiles and GS score, whereas serum PFOS showed no such association. Mechanistically, PFOS exposure promotes intestinal enrichment of B. caecimuris by upregulating its tolC gene, thereby enhancing efflux capacity. This microbial shift was accompanied by reduced levels of tauro-ursodeoxycholic acid (TUDCA) and aberrant activation of intestinal FXR signaling. Further analyses demonstrated that FXR activation upregulated TLR3 expression and promoted inflammatory responses and atherosclerosis progression via the TLR3-NF-{kappa}B signaling axis. Both intestinal epithelial-specific FXR deficiency (Fxr{Delta}IE) and TUDCA supplementation significantly suppressed pathway activation and alleviated disease phenotypes.Functional experiments identified TLR3 as a key downstream effector of FXR. Overexpression of TLR3 abolished the protective effects observed in Fxr{Delta}IE mice. Moreover, pharmacological inhibition of TLR3 using CU CPT-4a significantly improved established atherosclerotic lesions in vivo. CONCLUSIONS: This study identifies a gut microbiota-driven FXR-TLR3 signaling axis that mediates PFOS-induced atherosclerosis. These findings provide new mechanistic insights into environmentally induced cardiovascular disease and suggest potential targets for risk assessment and therapeutic intervention.

Background and AimsAs the older population (aged 65 years and older) continues to expand and more people drink alcohol, aging has been linked to the development of alcohol-associated liver disease (ALD) and to worse disease outcomes. The aim of this study was to explore the mechanisms by which advanced age and alcohol exacerbate alcohol-induced liver injury. MethodsTwo-to-three-month-old and twenty-to-twenty-two-month-old male C57BL/6N mice were subjected to chronic-on-binge alcohol feeding (the Gao-binge model). Serum alanine aminotransferase, aspartate aminotransferase, and triglyceride content were determined using biochemical assays. The levels of lipogenesis, fatty acid-metabolizing proteins, inflammatory markers, mitochondrial and autophagy-related proteins, and senescence-associated proteins were determined by immunoblotting, immunohistochemistry, and real-time quantitative polymerase chain reaction (RT-qPCR). Liver tissues were also subjected to RNA sequencing and metabolomics analyses. Proteomics analysis was performed on serum samples. Tail-vein adenovirus-TFEB was injected to overexpress hepatic TFEB in 22-month-old C57BL/6N male mice, followed by Gao-binge alcohol feeding. ResultsHepatic triglyceride content was significantly increased in aged, alcohol-fed mice, whereas serum ALT and AST levels remained relatively similar between alcohol-fed young and aged mice. Gao-binge alcohol increased the hepatic levels of diacylglycerol and acyl-carnitine species in both aged and young livers. RNA sequencing, proteomic analysis, and serum cytokine array analysis showed that inflammatory cytokines, including Ccr2, Cxcl1, and CCL6, and pro-inflammatory antibody fragments were increased in aged, alcohol-fed mice. Increased gene and protein expression of the senescent markers p21 and p27, along with increased senescent-associated (SA) {beta}-galactosidase activity in ethanol-fed aged mice compared to young mice. Gene and protein expression of TFEB was downregulated in ethanol-fed young and aged animals, along with decreased levels of lysosomal ATPases and hepatic dipeptide content. Overexpression of TFEB in the livers of aged, Gao-binge-fed mice was associated with reduced Ly6G-positive cells, reduced protein levels of the innate immune mediators cGAS, IRF-7, IRF3, and NLRP3, and reduced caspase-1 activity as well as serum ALT levels. ConclusionsOur findings indicate that advanced age perpetuates the detrimental effects of excessive alcohol consumption on various homeostatic processes and promotes steatosis and inflammation in the liver. Modulations in hepatic TFEB could be effective in mitigating pro-inflammatory signaling that occurs due to the synergistic effect of both heavy alcohol consumption and advanced age.

Comprehensive detection of steroids, beyond the limited panels typically analyzed in clinical chemistry laboratories, has become increasingly important given their pivotal roles in diverse biological processes. However, steroid quantification poses several analytical challenges, including differences in ionization efficiency and structural similarities across the entire steroid metabolic network. To address these challenges, we developed a targeted ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) assay to analyze 21 steroids using reverse-phase chromatography combined with rapid polarity switching. Mass spectrometry (MS) analysis was performed in scheduled multiple reaction monitoring (sMRM) mode. Depending on the steroid and matrix, the validated lower limits of quantitation (LLOQ) ranged from 12.0 pM to 1216 pM in plasma and 41.1 pM to 384 pM in fecal sample homogenates. In adipose tissue, it was from 0.01 pmol/g to 9 pmol/g. Measured steroid concentrations obtained from the commercial control samples (MassTrak Steroid Serum QC Set 1 and the MassCheck Steroid Panel 1 Serum Control) showed close agreement with the reference values. As a proof of concept, the method was successfully applied to 469 plasma samples in several projects, 15 adipose tissue samples, and 332 fecal samples, demonstrating its applicability to large-scale studies. In conclusion, the method enables sensitive, derivatization-free quantification of an expanded steroid panel in plasma and complex biological matrices, including adipose tissue and fecal samples, representing a significant advancement in comprehensive steroid profiling.

Background Besides hyperglycemia, type 2 diabetes (T2D) is characterized by dyslipidemia, which is typically assessed using traditional clinical lipid measurements. However, molecular plasma lipids beyond these traditional markers can provide additional information about an individuals health status. For molecular lipids to be used effectively, certain characteristics, such as their temporal variability, need to be determined. Methods We analyzed the plasma lipidome for three consecutive time points, each three months apart, of 51 individuals with T2D using targeted liquid chromatography coupled to mass spectrometry (LC-MS). 513 lipid species across 25 (sub)classes were quantified by this approach and the temporal variability were calculated. Moreover, to identify sex differences in the plasma lipidome, we analyzed 914 samples of a cross-sectional T2D cohort with the same approach. Results Neutral lipids and phosphatidylserine had the highest temporal variability which was independent of their platform-specific variability. In contrast, glycosphingolipids were found to be relatively stable over time in individuals with T2D. Acyl-chain analysis revealed generally similar variability in the acyl-chain groups but indicated a higher temporal variability in medium-length acyl-chains. Lipid-sex association analysis showed markedly higher sphingomyelins, phosphatidylcholines, and phosphatidylethanolamines in women and higher acylcarnitines in men. Overall, approximately one-third of measured lipids showed significant sex differences independent of age, BMI, diabetes duration, glycemic control, and medication use. Conclusions Our findings provide insights into temporal variability of molecular lipids. This variability should be considered when assessing novel lipid biomarkers. Likewise, sex differences in these lipids need to be considered in precision medicine for diabetes management.

Background: We propose the biological drift framework for the interpretation of biological test results: a z-score-like framework based on optimized and personalized reference populations and a distance-to-optimum drift metric for longitudinal interpretation relative to an estimated individual optimum. We benchmarked biological drifts against Reference Change Values (RCVs), which are used to interpret serial laboratory results by defining the minimum change expected to exceed normal within-subject biological variation CVi. Objectives: To benchmark biological drifts against the classical biological-variation framework and assess their consistency with RCV thresholds across routine biomarkers. Methods: For 62 routine biomarkers, biological drift levels were compared with RCVs after transformation to test the consistency between the two frameworks. Results: Severe biological drifts mostly exceeded the 95% RCV threshold, indicating changes unlikely to be explained by short-term biological variation alone. In contrast, moderate drifts reached the 95% RCV threshold for approximately one in two biomarkers, suggesting that many moderate distance-to-optimum deviations may remain within expected variability, particularly for biomarkers with large within-subject variation CVi. Results are particularly interesting for the follow-up of people with diabetes and for the management of thyroid and hepatic disorders. Conclusions: Biological drifts derived from optimized personalized reference populations are broadly consistent with the RCV framework for identifying biologically meaningful deviations from the optimum and may therefore be relevant for the monitoring of certain biomarkers across several medical conditions in clinical practice.

Background and aimsAlcohol-associated hepatitis (AH) is characterized by excessive inflammation and blunted antiviral interferon (IFN) responses. We hypothesized that specific gut microbiome-derived metabolites could selectively enhance interferon signaling while limiting NF-{kappa}B mediated inflammation, thereby restoring immune balance in AH. Our goal is to identify microbiome-derived metabolites that differentially regulate the NF-{kappa}B and IFN signaling pathways. Methods and resultsWe used human monocytic THP1-Dual cells, which secrete reporters for NF-{kappa}B and IFN signaling, to model innate immune responses and screened a library of 152 gut microbiome-derived metabolites. From the metabolite screen, 4-hydroxyphenylacetic acid (4-HPAA) emerged as a unique immunomodulator: in LPS-challenged cells, 4-HPAA selectively increased IFN signaling with minimal NF-{kappa}B activation. 4-HPAA was evaluated in vivo using a NIAAA-model, with 4-HPAA supplementation (0.4mg/ml) added to the diet. In the NIAAA-model, dietary 4-HPAA did not induce liver injury and was associated with enhanced interferon-stimulated gene expression. Simultaneously, 4-HPAA reduced pro-inflammatory markers such as Il1{beta}, Ly6g and F4/80 compared to the group exposed to ethanol alone. Metabolomic profiling of mouse cecal contents revealed 4-HPAA supplementation counteracted ethanols metabolic effects, selectively reducing triglyceride-associated lipids that had accumulated with ethanol feeding. Conclusions4-HPAA enhances interferon signaling and antiviral gene induction while dampening NF-{kappa}B-driven inflammation in the presence of LPS, both in vitro and in vivo. In an acute-on-chronic alcohol injury model, 4-HPAA attenuated hepatic inflammation, reduced immune cell recruitment, and activated antioxidant defenses, reflecting a shift toward a more hepatoprotective effect. 4-HPAA treatment was associated with reduced pro-inflammatory markers and modest attenuation of ethanol-induced liver injury. Additionally, 4-HPAA reversed ethanol-induced lipid-dysregulation, particularly triglyceride accumulation, highlighting its metabolic benefit in alcohol-fed mice. In conclusion, 4-HPAA rebalances immune and metabolic pathways by enhancing IFN signaling, suppressing NF-{kappa}B inflammation, and reversing alcohol-induced hepatic injury and lipid accumulation.

Objectives; The role of nuclear receptor subfamily 4, group A, member 3 (NR4A3) in hepatic steatosis, inflammation, and insulin resistance (IR) within the context of metabolic dysfunction-associated steatotic liver disease (MASLD) remains largely underexplored. Consequently, this study aimed to examine NR4A3's impact on MASLD and the potential underlying mechanisms. Methods; We aimed to elucidate the functional role of NR4A3 in MASLD through its knockdown in cell culture and animal models. To establish the cell culture model of MASLD, LO2 cells were treated with free fatty acids (FFAs), while male C57BL/6 mice were fed a high-fat diet (HFD) to create the animal model. NR4A3 knockdown was achieved using specific short hairpin RNA (NR4A3-shRNA) in the mice model and three small interfering RNAs (NR4A3-siRNAs) in the cell culture model. The lipids content, fatty acid synthesis, inflammatory factors, and IR were then assessed with and without NR4A3 knockdown. Furthermore, the underlying mechanism through which NR4A3 exerts its influence was explored by analyzing the interaction between NR4A3 and activating transcription factor 3 (ATF3). Results: In the cell culture experiments, the knockdown of NR4A3 significantly decreased the lipids content, fatty acid synthesis, and inflammatory factors in the LO2 cells treated with FFAs in the NR4A3-shRNA group compared with those in the NC-shRNA control group. In the animal model experiments, NR4A3 knockdown in the HFD male C57BL/6 mice significantly ameliorated HFD-induced hepatic steatosis, inflammation, and IR. Mechanistically, the knockdown of NR4A3 downregulated the expression and transcriptional activity of ATF3, resulting in an impaired ATF3 function. ATF3 overexpression significantly reversed lipid accumulation decline and reduced inflammation after NR4A3 knockdown. Conclusion: The downregulation of NR4A3 alleviates MASLD by modulating ATF3, suggesting this may be a promising therapeutic target.

Purpose Metabolic dysfunction-associated steatotic liver disease (MASLD) is a major cause of chronic liver disease and liver-related morbidity worldwide. Although dietary factors may influence MASLD progression, the long-term liver-specific implications of artificially sweetened beverage (ASB) intake remain unclear. We aimed to examine the association between ASB intake and the risk of liver-related adverse events and liver-related death among individuals with MASLD. Methods This prospective cohort study included 50,562 participants with MASLD from the UK Biobank. ASB intake was assessed using 24-hour dietary recalls and categorized as 0, >0-1, and >1 serving/day. Multivariable Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for liver-related adverse events and liver-related death. Restricted cubic spline models were used to assess dose-response patterns, and competing-risk analyses were performed by treating liver-related death as a competing event for liver-related adverse events. Additional substitution, subgroup and sensitivity analyses were conducted to evaluate the robustness of the findings. Results During a median follow-up of 12.8 years, 292 liver-related adverse events and 91 liver-related deaths occurred. Compared with participants reporting no ASB intake, those consuming >1 serving/day had a higher risk of liver-related adverse events in the fully adjusted model (HR 1.40, 95% CI 1.02-1.93; P = 0.039), whereas the association for >0-1 serving/day was not statistically significant (HR 1.26, 95% CI 0.92-1.71; P = 0.149). The risk of liver-related adverse events increased across ASB intake categories (P for trend = 0.023). Restricted cubic spline analysis indicated a positive linear association between ASB intake and liver-related adverse events (P-overall <0.001; P-nonlinearity = 0.72). In competing-risk analysis, the association for >1 serving/day remained consistent after accounting for liver-related death as a competing event (sub-HR 1.40, 95% CI 1.02-1.93; P = 0.038; Gray test P = 0.006). The association was robust in sensitivity analyses. ASB intake was not significantly associated with liver-related death, and beverage substitution analyses showed no significant associations. Conclusion Among individuals with MASLD, high ASB intake, particularly >1 serving/day, was associated with an increased risk of liver-related adverse events, but not liver-related death. This association was consistent across dose-response, competing-risk, and sensitivity analyses, suggesting that high ASB intake may represent a potential dietary risk marker for adverse liver outcomes in MASLD.

Seed quality is a complex trait shaped by morphological, biochemical and microbiological properties that are rarely characterised simultaneously, limiting our ability to identify robust predictive indicators of germination speed and seedling emergence across varieties. Here, we performed a multi-factor characterisation of eight common bean (Phaseolus vulgaris L.) varieties, combining seed morphometrics, untargeted GC-MS metabolomics on three seed organs, and amplicon sequencing of bacterial and fungal communities, to identify indicators of germination speed and emergence percentage. The eight varieties showed substantial variation in both traits, used as physiological seed quality proxies. Seed weight and size variation between varieties were correlated with germination speed. The intravariety variance of seed weight was independently correlated with emergence performance. Metabolome composition differed strongly across seed organs, with variety as the dominant driver. Individual-seed metabolomic profiles in the plumule and cotyledon were associated with germination speed but not emergence, yielding 16 plumule and three cotyledon candidate metabolite markers. Fungal community composition was associated with both germination speed and emergence, while bacterial communities were associated with emergence only. Nine fungal and four bacterial taxa were identified as candidate indicators. Inter-kingdom co-occurrence network analysis revealed that fungi with similar germination speed associations tend to cluster in the same modules, suggesting that community-level modules rather than individual taxa may constitute more robust microbial indicators. These results demonstrate that germination speed and emergence capacity are governed by distinct seed properties, and provide morphological, metabolic and microbial candidate indicators for integration into targeted seed quality assessment frameworks for common bean.

Obesity and related conditions such as dyslipidemia impose an increasing burden on healthcare systems worldwide. These conditions are associated with altered postprandial chylomicron (CM) metabolism, the elusive and critical first step in lipid metabolism. This step remains elusive because it is governed by large interindividual variations and a complex set of intestinal processes. In particular, the second meal effect (SME) implies that enterocytes release previously stored fat during subsequent meals. To deal with this complexity, CM and lipid metabolism have previously been explored using mathematical modeling. However, existing models primarily describe TAG dynamics following a single meal or are too complex for practical personalization across datasets. Herein, we address these limitations by presenting a small-scale mathematical model of CM dynamics that incorporates the SME. The presented model successfully describes data from six clinical studies of both single and repeated meal interventions. Model performance was further evaluated by predicting independent datasets using a BMI-dependent calibration. Finally, to demonstrate model applicability, we simulated full-day responses consisting of three sequential meals in individuals with varying BMI values, with qualitative agreement to clinical observations. This work supports our understanding of the SME, person-specific CM postprandial responses, and mechanisms underlying obesity.

Background: Early detection of pancreatic ductal adenocarcinoma (PDAC) is critical. While faecal elastase-1 (FE-1) is a standard clinical marker for pancreatic function, its diagnostic accuracy for malignancy is limited. We sought to identify plasma metabolites that enhance FE-1 performance in symptomatic "at-risk" patients. Methods: Using the DEPEND cohort (CRUK C45617/A29908), plasma metabolomics was performed on patients with resectable PDAC (n=23) and healthy volunteers (n=24). Predictive modelling included feature selection and cross-validation, with further validation in an independent external cohort. Results: Citrulline was identified as significantly depleted in PDAC patients across discovery and validation cohorts. In isolation, Citrulline achieved an AUC of 0.86 (internal) and 0.88 (external validation). Standalone FE-1 demonstrated an AUC of 0.67. However, combining Citrulline and FE-1 significantly improved diagnostic performance, achieving a combined AUC of 0.96. Stratification revealed distinct metabolomic signatures associated with poorly differentiated tumours, suggesting a link to histological grade. Conclusions: Integrating Citrulline with FE-1 testing substantially improves PDAC detection in symptomatic patients. This non-invasive panel offers high diagnostic potential, though prospective validation is required to establish clinical cut-offs for routine practice.

Background chemical ions are a pervasive but often underappreciated limitation in LC-MS metabolomics, where they can suppress analyte signal, obscure endogenous metabolites, increase spectral complexity, and consume MS/MS acquisition events. Tributylamine (TBA) ion-pairing reversed-phase LC-MS provides stable retention and broad coverage of polar anionic metabolites, including central carbon intermediates, nucleotides, cofactors, and bile acids, but the back-ground burden introduced by the ion-pairing reagent itself has not been systematically addressed. Here, we identify commercial TBA as a major source of nonbiological contaminant ions and develop a practical strategy to reduce background burden while preserving metabolite coverage. Serial solid-phase extraction of TBA using orthogonal reversed-phase, strong anion-exchange, and strong cation-exchange sorbents removed chemically diverse contaminants, including isobaric background ions that interfered with endogenous hydroxybutyrate isomers. We further optimized the workflow by reducing medronic acid concentration, restricting medronic acid to the organic mobile phase, replacing phosphoric-acid column conditioning with metal-passivated column hardware, and adding EDTA to the sample reconstitution solvent to improve citrate detection. In mouse liver extracts, the optimized method increased signal intensity for most annotated metabolites and improved the fraction of full-scan ion current attributable to target analytes. Method optimization also altered compound-specific retention behavior, resolving some co-elution-based interferences while introducing new suppression relationships for selected analytes. Across mouse liver, human B lymphocytes, and NIST SRM 1950 plasma, the optimized workflow increased total feature detection by 45%, 72%, and 42%, respectively, and improved the number of low-variance features, precursors with data-dependent MS/MS spectra, and MS/MS library matches. These findings establish background-ion mitigation as a central design principle for LC-MS method development. More broadly, this work provides a generalizable framework for identifying, reducing, and validating reagent- and additive-derived background to improve targeted and untargeted LC-MS data quality.

Background: Elevated C-Reactive Protein (CRP), interleukin-6 (IL-6) and testosterone deficiency are associated with advanced age and chronic inflammatory diseases; while normal testosterone levels have been shown to decrease inflammation through several mechanisms. Cross-sectional studies have shown an inverse relationship between CRP, IL-6 and total testosterone (TT) levels, yet mixed findings have been reported when individual components of metabolic syndrome are considered. We evaluated the relationship between CRP, IL-6 and TT levels in men from 2004-2018 using the Baltimore Longitudinal Study of Aging to determine if low testosterone status is associated with a high inflammatory profile. Methods: Participants were selected from the Baltimore Longitudinal Study of Aging. Male participants with serum TT level measured during at least three visits were included in our cohort. Common measures of inflammatory disease such as CRP, High-Density Lipoprotein (HDL) and Triglyceride levels were collected via blood specimens. Comorbidity data were documented at each visit. Panel regression was used to analyze the relationship of a series of independent variables collected in pooled cross-sectional observations over time with a dependent variable for modeling. Results: A total of 347 patients were included in this study (median age = 70, IQR = 18, average follow up time = 6.7 +/- 3.2 years). Participants had a median CRP level of 1.0 mg/dL, median IL-6 level of 3.6, a median TT level of 446 ng/dL. On univariable analysis, increasing TT and HDL levels were associated with a decline in CRP, while high Body Mass Index (BMI), congestive heart failure (CHF), Diabetes, and increased serum triglycerides were associated with increased CRP. Age was not associated with CRP. On multivariable analysis, we found that increasing TT level was associated with a decline in CRP levels, independent of comorbidities (p = 0.018; Table 1). As expected, increased BMI was associated with a significant increase in CRP (p = 0.001, Table 1). Age, CHF, Diabetes, HDL, and Triglycerides were not significant predictors of CRP on multivariable analysis. Similarly, on multivariable analysis, increasing TT levels were independently associated with lower IL-6 levels. Higher HDL cholesterol levels were also associated with lower IL-6 levels, whereas increasing age was associated with higher IL-6 levels. BMI, CHF, diabetes, and triglycerides were not significant predictors of IL-6. Conclusions: Lower levels of serum total testosterone are associated with an increase in CRP in older men over time, independent of chronic inflammatory disease. Given the importance of CRP in pathogenesis of chronic disease, we highlight the potential benefits of using total testosterone as a biomarker of chronic inflammatory states.