Metabolomic Signatures Research Articles

Multiomics approach discloses lipids and metabolites profiles associated to Parkinson's disease stages and applied therapies

Profiling circulating lipids and metabolites in Parkinson's disease (PD) patients could be useful not only to highlight new pathways affected in PD condition but also to identify sensitive and effective biomarkers for early disease detection and potentially effective therapeutic interventions. In this study we adopted an untargeted omics approach in three groups of patients (No L-Dopa, L-Dopa and DBS) to disclose whether long-term levodopa treatment with or without deep brain stimulation (DBS) could reflect a characteristic lipidomic and metabolomic signature at circulating level. Our findings disclosed a wide up regulation of the majority of differentially regulated lipid species that increase with disease progression and severity. We found a relevant modulation of triacylglycerols and acyl-carnitines, together with an altered profile in adiponectin and leptin, that can differentiate the DBS treated group from the others PD patients. We found a highly significant increase of exosyl ceramides (Hex2Cer) and sphingoid bases (SPB) in PD patients mainly in DBS group (p < 0.0001), which also resulted in a highly accurate diagnostic performance. At metabolomic level, we found a wide dysregulation of pathways involved in the biosynthesis and metabolism of several amino acids. The most interesting finding was the identification of a specific modulation of L-glutamic acid in the three groups of patients. L-glutamate levels increased slightly in No L-Dopa and highly in L-Dopa patients while decreased in DBS, suggesting that DBS therapy might have a beneficial effect on the glutamatergic cascade. All together, these data provide novel insights into the molecular and metabolic alterations underlying PD therapy and might be relevant for PD prediction, diagnosis and treatment.

Characterizing metabolome signature of colostrum, transition and mature milk of indigenous cows (Bos indicus) adapted to high altitude environment of Leh-Ladakh

This study has identified 46 metabolites in colostrum, transition milk and mature milk of unique indigenous high altitude adapted Ladakhi cows using 1D 1H 800 MHz NMR spectroscopy. The multivariate analysis revealed that UDP-galactose, UDP-glucose, citrate, creatine phosphate, myo-inositol, lactose, 2-oxoglutarate, valine, maltose, leucine, dimethylamine, and choline with high VIP scores could differentiate the colostrum, transition and mature milk in separate clusters. . Highly enriched metabolites in colostrum such as UDP-galactose, UDP-glucose play crucial roles in cell growth, differentiation, and defense responses. Similarly, the presence of branched chain amino acids in colostrum could be linked to mammary gland development, N-acetylglucosamine, N-acetyl carnitine, choline etc. in high concentration in colostrum l might be helping in growth and development of neonatal calves of Ladakhi cows under hypoxia environment. Overall, this study has helped to characterize the metabolomic signatures of milk/colostrum of Ladakhi cows adapted to high altitude and cold desert of Leh-Ladakh.

Biochanin-A co-crystal formulation improves bioavailability and ameliorates cerulein-induced pancreatitis by attenuating the inflammation

Co-crystallization of a therapeutic ingredient with an appropriate co-former is a powerful technique to augment the physicochemical and pharmacokinetic properties and the effectiveness of Active Pharmaceutical Ingredients (APIs). Biochanin A (BCA), a flavonoid with medicinal potential, is limited by poor solubility and low oral bioavailability. This study aimed to design and develop a novel BCA-nicotinamide cocrystal as BCC to enhance BCA’s oral bioavailability and explore its therapeutic potential for ameliorating cerulein-induced acute pancreatitis (CIAP) by elucidating the target identification utilizing tissue/serum metabolite profiles. The cocrystal was designed by the supramolecular synthon approach and characterized by single-crystal X-ray diffraction that confirms a robust three-dimensional hydrogen-bonded network of BCA and Nicotinamide (NCT) in the crystal. FT-IR and DSC were used to analyze the cocrystal’s intermolecular interactions and thermal behavior. BCC exhibited enhanced solubility and drug release compared to BCA alone, resulting in enhanced oral bioavailability and pancreatic tissue concentration. Comparing BCC to BCA in the CIAP model, BCC therapy remarkably reduced cerulein-induced pancreatitis, evidenced by significant reductions in inflammation, acinar cell atrophy, and amylase levels in pancreatic tissues. Further, the cocrystal formulation also down-regulated the oxidative stress markers, inflammatory cytokines and macrophage-related proteins. The study has identified distinct metabolomic signatures linked with AP with the help of Orbitrap Exploris mass spectrometry, which could pave the way for creating focused diagnostic tools for a better prognosis. In conclusion, these results offer new insights into exploring mechanistic pathways associated with specific biomarkers and underscore BCC cocrystals as a promising approach to enhance BCA’s therapeutic potential.

Metabolomic signature identifies HDL and apolipoproteins as potential biomarker for systemic sclerosis with interstitial lung disease

BackgroundHigh-density lipoproteins (HDL) affect endothelial functions such as the expression of endothelial cell adhesion molecules and exert anti-apoptotic/-thrombotic functionalities. Therefore, profound analysis of lipoproteins may unveil biomarkers for (micro-)vasculopathy in systemic sclerosis (SSc) and mortality determining disease manifestations like interstitial lung disease (SSc-ILD). Because nuclear magnetic resonance (NMR) spectroscopy provides a wide range of lipoprotein parameters beyond the capabilities of classical analyses it has been used herein to examine lipoprotein profiles in SSc. MethodsTo detect the metabolic and lipidomic profile serum samples from clinically well-characterized SSc patients (n = 100) and age-and sex-matched healthy controls (n = 40) were analyzed by 1H NMR spectroscopy using Bruker's in-vitro diagnostic research (IVDr) protocol. Statistical analyses were performed to validate significant findings and to search for associations between lipoproteins and clinical phenotypes. ResultsPatients with SSc-ILD and lung fibrosis displayed reduced HDL levels. Furthermore, a reduction in apolipoprotein A1 + A2 and its HDL fractions reflected a distinct lipoprotein profile for SSc-ILD patients. This association was independent of potential clinical confounders for dyslipidemia. Notably, in SSc-ILD HDL levels correlate with FVC (forced vital capacity), DLCO (diffusion capacity of the lungs for carbon monoxide), and the modified Rodnan-Skin-Score. ConclusionThese results suggest HDL and its lipoproteins may be considered as potential new biomarkers for SSc-ILD. Immune-mediated HDL effects on the endothelium facilitate microvasculopathy - one of the pathophysiological hallmarks in SSc. Therefore, a closer prospective evaluation of the capability of HDL-determination and its lipoproteins regarding a more individualized evaluation of SSc-ILD is warranted.

Plasma metabolomic signature of healthy lifestyle, structural brain reserve and risk of dementia.

Although the association between healthy lifestyle and dementia risk has been documented, the relationship between a metabolic signature indicative of healthy lifestyle and dementia risk and the mediating role of structural brain impairment remain unknown. We retrieved 136 628 dementia-free participants from UK Biobank. Elastic net regression was used to obtain a metabolic signature that represented lifestyle behaviours. Cox proportional hazard models were fitted to explore the associations of lifestyle-associated metabolic signature with incident dementia. Causal associations between identified metabolites and dementia were investigated using Mendelian randomization. Mediation analysis was also conducted to uncover the potential mechanisms involving 19 imaging-derived phenotypes (brain volume, grey matter volume, white matter volume and regional grey matter volumes). During a follow-up of 12.55 years, 1783 incident cases of all-cause dementia were identified, including 725 cases of Alzheimer's dementia and 418 cases of vascular dementia. We identified 83 metabolites that could represent healthy lifestyle behaviours using elastic net regression. The metabolic signature was associated with a lower dementia risk, and for each standard deviation increment in metabolic signature, the hazard ratio was 0.89 [95% confidence interval (CI): 0.85, 0.93] for all-cause dementia, 0.95 (95% CI: 0.88, 1.03) for Alzheimer's dementia and 0.84 (95% CI: 0.77, 0.91) for vascular dementia. Mendelian randomization revealed potential causal associations between the identified metabolites and risk of dementia. In addition, the specific structural brain reserve, including the hippocampus, grey matter in the hippocampus, parahippocampal gyrus and middle temporal gyrus, were detected to mediate the effects of metabolic signature on dementia risk (mediated proportion ranging from 6.21% to 11.98%). The metabolic signature associated with a healthy lifestyle is inversely associated with dementia risk, and greater structural brain reserve plays an important role in mediating this relationship. These findings have significant implications for understanding the intricate connections between lifestyle, metabolism and brain health.

Nuclear magnetic resonance-based metabolomics with machine learning for predicting progression from prediabetes to diabetes.

Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes. This prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were utilized to validate the predictive ability of the metabolites. During a median follow-up of 13.6 years, 2525 participants developed diabetes. After adjusting for covariates, 94 of 168 metabolites were associated with risk of progression to diabetes. A panel of nine metabolites, selected by all three machine-learning algorithms, was found to significantly improve diabetes risk prediction beyond conventional risk factors in the CPH model (area under the receiver-operating characteristic curve, 1 year: 0.823 for risk factors + metabolites vs 0.759 for risk factors, 5 years: 0.830 vs 0.798, 10 years: 0.801 vs 0.776, all p < 0.05). Similar results were observed from the RSF model. Categorization of participants according to the predicted value thresholds revealed distinct cumulative risk of diabetes. Our study lends support for use of the metabolite markers to help determine individuals with prediabetes who are at high risk of progressing to diabetes and inform targeted and efficient interventions. Shanghai Municipal Health Commission (2022XD017). Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212501). Shanghai Municipal Human Resources and Social Security Bureau (2020074). Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR4006). Science and Technology Commission of Shanghai Municipality (22015810500).

Deciphering metabolomics and lipidomics landscape in zebrafish hypertrophic cardiomyopathy model

To elucidate the lipidomic and metabolomic alterations associated with hypertrophic cardiomyopathy (HCM) pathogenesis, we utilized cmybpc3-/- zebrafish model. Fatty acid profiling revealed variability of 10 fatty acids profiles, with heterozygous (HT) and homozygous (HM) groups exhibiting distinct patterns. Hierarchical cluster analysis and multivariate analyses demonstrated a clear separation of HM from HT and control (CO) groups related to cardiac remodeling. Lipidomic profiling identified 257 annotated lipids, with two significantly dysregulated between CO and HT, and 59 between HM and CO. Acylcarnitines and phosphatidylcholines were identified as key contributors to group differentiation, suggesting a shift in energy source. Untargeted metabolomics revealed 110 and 53 significantly dysregulated metabolites. Pathway enrichment analysis highlighted perturbations in multiple metabolic pathways in the HM group, including nicotinate, nicotinamide, purine, glyoxylate, dicarboxylate, glycerophospholipid, pyrimidine, and amino acid metabolism. Our study provides comprehensive insights into the lipidomic and metabolomic unique signatures associated with cmybpc3-/- induced HCM in zebrafish. The identified biomarkers and dysregulated pathways shed light on the metabolic perturbations underlying HCM pathology, offering potential targets for further investigation and potential new therapeutic interventions.

Metabolomic characterization of human glioblastomas and patient plasma: a pilot study

Background Glioblastoma (GBM) is a clinically challenging primary brain tumor with poor survival outcome despite surgical resection and intensive chemoradiation. The metabolic heterogeneity of GBM can become biomarkers for treatment response, resistance, and outcome prediction. The aim of the study is to investigate metabolic distinctions between primary and recurrent GBM tissue and patient plasma to establish feasibility for metabolic profiling. Methods A single-center cohort study analyzed tissue and blood samples from 15 patients with GBM using untargeted metabolomic/lipidomic assays. Metabolomic, lipidomic, and biogenic amine analyses were conducted on GBM tissue and patient plasma at diagnosis and recurrence using untargeted mass spectrometry. The study utilized a small but longitudinally collected cohort to evaluate alteration in metabolites, lipids, and biogenic amines between specimens at diagnosis and recurrence. Results Exploratory analysis revealed significant alteration in metabolites, lipids, and biogenic amines between diagnostic and recurrent states in both tumor and plasma specimens. Notable metabolites differed at recurrence, including N-alpha-methylhistamine, glycerol-3-phosphate, phosphocholine, and succinic acid in tissue, and indole-3-acetate, and urea in plasma. Principal component analysis revealed distinct metabolomic profiles between tumor tissue and patient plasma. Distinct metabolic profiles were observed in GBM tissue and patient plasma at recurrence, demonstrating the feasibility of using metabolomic methodologies for longitudinal studies. One patient exhibited a unique tumor resistance signature at diagnosis, possibly indicating a high-risk metabolomic phenotype. Conclusions In this small cohort, the findings suggest the potential of metabolomic signatures of GBM tissue and patient plasma for risk stratification, outcome prediction, and the development of novel adjuvant metabolic-targeting therapies. The findings suggest metabolic discrepancies at diagnosis and recurrence in tissue and plasma, highlighting potential implications for evaluation of clinical response. The identification of significant changes in metabolite abundance emphasizes the need for larger studies using targeted metabolomics to validate and further explore these profiles.

Intestinal-level anti-inflammatory bioactivities of whole wheat: Rationale, design, and methods of a randomized, controlled, crossover dietary trial in adults with prediabetes

Randomized controlled trials (RCT) demonstrate that whole wheat consumption improves glycemia. However, substantial inter-individual variation is often observed, highlighting that dietary whole grain recommendations may not support the health of all persons. The objective of this report is to describe the rationale and design of a planned RCT aimed at establishing the gut microbiota and metabolome signatures that predict whole wheat-mediated improvements in glucose tolerance in adults with prediabetes. It is hypothesized that a controlled diet containing wheat bread (WHEAT; 160 g/day) compared with refined bread (WHITE) will improve glucose tolerance in a gut microbiota-mediated manner. Biospecimens will be collected before and after each 2-week study arm. Testing for oral glucose tolerance and gastrointestinal permeability will be performed post-intervention. Assessments will include oral glucose tolerance (primary outcome) and secondary outcomes including gut microbiota, targeted and untargeted metabolomics of fecal and plasma samples, intestinal and host inflammatory responses, and intestinal permeability. WHEAT is predicted to alleviate glucose intolerance by shifting microbiota composition to increase short-chain fatty acid-producing bacteria while reducing populations implicated in intestinal inflammation, barrier dysfunction, and systemic endotoxemia. Further, benefits from WHEAT are anticipated to correlate with gut-level and systemic metabolomic responses that can help to explain the expected inter-individual variability in glucose tolerance. Thus, knowledge gained from integrating multi-omic responses associating with glucose tolerance could help to establish a precision nutrition-based framework that can alleviate cardiometabolic risk. This framework could inform novel dietary whole grain recommendations by enhancing our understanding of inter-individual responsiveness to whole grain consumption.

Tissue-specific metabolomic signatures for a doublesex model of reduced sexual dimorphism.

Sex has a major effect on the metabolome. However, we do not yet understand the degree to which these quantitative sex differences in metabolism are associated with anatomical dimorphism and modulated by sex-specific tissues. In the fruit fly, Drosophila melanogaster, knocking out the doublesex (dsx) gene gives rise to adults with intermediate sex characteristics. Here we sought to determine the degree to which this key node in sexual development leads to sex differences in the fly metabolome. We measured 91 metabolites across head, thorax and abdomen in Drosophila, comparing the differences between distinctly sex-dimorphic flies with those of reduced sexual dimorphism: dsx null flies. Notably, in the reduced dimorphism flies, we observed a sex difference in only 1 of 91 metabolites, kynurenate, whereas 51% of metabolites (46/91) were significantly different between wildtype XX and XY flies in at least one tissue, suggesting that dsx plays a major role in sex differences in fly metabolism. Kynurenate was consistently higher in XX flies in both the presence and absence of functioning dsx. We observed tissue-specific consequences of knocking out dsx. Metabolites affected by sex were significantly enriched in branched chain amino acid metabolism and the mTOR pathway. This highlights the importance of considering variation in genes that cause anatomical sexual dimorphism when analyzing sex differences in metabolic profiles and interpreting their biological significance.

Protein-Based Predictive Biomarkers to Personalize Neoadjuvant Therapy for Bladder Cancer-A Systematic Review of the Current Status.

The clinical outcome of patients with muscle-invasive bladder cancer (MIBC) is poor despite the approval of neoadjuvant chemotherapy or immunotherapy to improve overall survival after cystectomy. MIBC subtypes, immune, transcriptome, metabolomic signatures, and mutation burden have the potential to predict treatment response but none have been incorporated into clinical practice, as tumor heterogeneity and lineage plasticity influence their efficacy. Using the PRISMA statement, we conducted a systematic review of the literature, involving 135 studies published within the last five years, to identify studies reporting on the prognostic value of protein-based biomarkers for response to neoadjuvant therapy in patients with MIBC. The studies were grouped based on biomarkers related to molecular subtypes, cancer stem cell, actin-cytoskeleton, epithelial-mesenchymal transition, apoptosis, and tumor-infiltrating immune cells. These studies show the potential of protein-based biomarkers, especially in the spatial context, to reduce the influence of tumor heterogeneity on a biomarker's prognostic capability. Nevertheless, currently, there is little consensus on the methodology, reagents, and the scoring systems to allow reliable assessment of the biomarkers of interest. Furthermore, the small sample size of several studies necessitates the validation of potential prognostic biomarkers in larger multicenter cohorts before their use for individualizing neoadjuvant therapy regimens for patients with MIBC.

Glutaminolysis provides nucleotides and amino acids to regulate osteoclast differentiation in mice

Osteoclasts are bone resorbing cells that are essential to maintain skeletal integrity and function. While many of the growth factors and molecular signals that govern osteoclastogenesis are well studied, how the metabolome changes during osteoclastogenesis is unknown. Using a multifaceted approach, we identified a metabolomic signature of osteoclast differentiation consisting of increased amino acid and nucleotide metabolism. Maintenance of the osteoclast metabolic signature is governed by elevated glutaminolysis. Mechanistically, glutaminolysis provides amino acids and nucleotides which are essential for osteoclast differentiation and bone resorption in vitro. Genetic experiments in mice found that glutaminolysis is essential for osteoclastogenesis and bone resorption in vivo. Highlighting the therapeutic implications of these findings, inhibiting glutaminolysis using CB-839 prevented ovariectomy induced bone loss in mice. Collectively, our data provide strong genetic and pharmacological evidence that glutaminolysis is essential to regulate osteoclast metabolism, promote osteoclastogenesis and modulate bone resorption in mice.

308 Isoenergetic reduction of dietary macronutrients reveals distinct fecal metabolomic signatures in lean and obese cats fed at maintenance

Abstract Obesity has become a concerning problem in cats. Increasing evidence demonstrates the utility of metabolomics in identifying disease biomarkers and assessing the effects of nutritional interventions, although limited information exists regarding alterations in fecal metabolites in lean and obese domestic cats in response to different macronutrient content. In the current study, three extruded diets were formulated based on adult maintenance using an isoenergentic approach. These diets were created by adjusting the levels of identical ingredients to achieve a low-protein [LP; protein 28% metabolizable energy (ME), fat 40%ME, NFE 32% ME), a low-fat (LF; protein 42% ME, fat 30% ME, NFE 30% ME), and a low-carbohydrate (LC; protein 36% ME, fat 41% ME, NFE 23% ME] diet. Healthy neutered male adult cats [n = 18; body condition scores (BCS) 4 or 5 (lean, n = 9) and 8 or 9 (obese, n = 9)] were fed each diet for a period of 4 wk in a 3 x 3 Latin square design. Fecal samples from each cat were collected between d 22 and 28 of each treatment period. A total of 67 fecal metabolites in six groups (35 amino acids, 11 fatty acids, 10 sugars and sugar metabolites, 5 alcohols, 3 nitrogenous bases, and 3 others) were analyzed using 1H NMR spectroscopy. To evaluate the impact of body condition, diet and their interaction on each metabolite, proc GLIMMIX covariance structure was selected by the smallest Akaike information criterion value in SAS. The Shapiro-Wilk test was used to check normality and lognormal or beta distribution were applied when appropriate. Bonferroni correction post-hoc test was followed to correct Type I error. Statistical significance was considered at P &amp;lt; 0.05. In lean cats, significantly greater concentrations of fecal proteinogenic and branched-chain amino acids, such as methionine, valine, and L-lysine, as well as pyruvic acid and sugars (P &amp;lt; 0.05) were observed. This suggests that these metabolites may either be more actively fermented by bacteria or not utilized as extensively as in obese cats, potentially contributing to a reduction in obesity status. Regardless of body condition, there is a possibility of an enhanced activity in the tryptophan degradation pathway, leading to the greatest concentrations of fecal tryptophan (P = 0.04) in the LF diet, while the greatest fecal D-galactose (P = 0.04) in the LP diet was likely due to the greater dietary carbohydrate content. These observed results suggested that fecal metabolites were influenced by both body condition and diet; however, body condition had a more substantial impact on metabolite changes in cats, whereas no body condition and diet interaction were noted. Overall, the study indicated distinct fecal metabolic signatures in lean and obese cats subjected to different macronutrient diets and further research is warranted to validate the observed findings. Funding was provided by Champion Petfoods, Natural Sciences and Engineering Council of Canada and Mitacs Accelerate.

Red blood cell metabolomics identify ergothioneine as a key metabolite in DMARD-naïve rheumatoid arthritis and response to methotrexate

Using a new red blood cell (RBC) metabolite extraction protocol, we performed a metabolomic analysis on RBCs in rheumatoid arthritis (RA) patients treated or not with methotrexate (MTX), with the two following objectives: to compare the RBC metabolic profiles of MTX-naïve RA patients and healthy controls (HC), and to investigate whether RBC profiles before and after MTX treatment in RA differed between responders and non-responders. Plasma analysis was performed in parallel. Metabolites were extracted and identified in RBCs and plasma by liquid chromatography-mass spectrometry. We compared the metabolomic fingerprints of 31 DMARD-naïve RA patients and 39 HCs. We also compared the RBC and plasma metabolomes of 25 RA patients who responded or not to MTX therapy before (M0) and after a 3-month treatment period (M3). Significance was determined by Storey’s false discovery rate (FDR) q-values to correct for multiple testing. RA patients and HCs differed in the metabolomic signature of RBCs. The signature mainly contained amino acids (AA). Eleven metabolites, including 4 metabolites belonging to the carbohydrate subclass and 2 amino acids (creatine and valine) showed accumulation in RBCs from RA patients. Conversely, citrulline (fold change = 0.83; q = 0.025), histidine (fold change = 0.86; q = 0.014) and ergothioneine (EGT) (fold change = 0.66; q = 0.024), were lower in RBC of RA patients. Five plasma metabolites, including succinic acid and hydroxyproline, were higher in RA patients, and 7 metabolites, including DHEA sulfate, alanine, threonine and ornithine, were lower. Among RA patients undergoing MTX treatment pre-treatment (M0), EGT values were significantly lower in non-responders. In conclusion, low RBC levels of EGT, a food-derived AA barely detectable in plasma, characterize DMARD naïve RA patients and lack of response to MTX treatment.

Metagenomics Analysis Reveals Unique Gut Microbiota Signature of Slow-Transit Constipation.

Altered gut microbiota may play a role in slow-transit constipation (STC). We conducted a study of gut microbiota composition and functionality in STC using metagenomic analyses. We assembled a clinical cohort of 24 patients with STC physiology age- and sex-matched to 24 controls. We performed shotgun metagenomic sequencing followed by prediction of metabolite composition from functional profiles. In a middle-aged (mean 55.3 years), predominantly female cohort, there were no significant differences in α-diversity indices, but permutational multivariate analysis of variance analysis showed significant between-group differences (R 2 = 0.050, P < 0.001) between STC patients and controls. Gordonibacter pamelaeae , Bifidobacterium longum , Firmicutes bacterium co-abundance gene group 94, and Anaerotruncus colihominis were more abundant in STC, whereas Coprococcus comes and Roseburia intestinalis were more abundant in controls. Gut-derived metabolites varying in STC relative to controls were related to bile acid and cholesterol metabolism. We found a unique metagenomic and metabolomic signature of STC.

492P Linking cellular drug responses to corresponding metabolomic tissue signatures in gliomas

492P Linking cellular drug responses to corresponding metabolomic tissue signatures in gliomas

Metabolomic profiles differentiate between porto-sinusoidal vascular disorder, cirrhosis, and health

Background & AimsPorto-sinusoidal vascular disorder (PSVD) is a rare and diagnostically challenging vascular liver disease. This study aimed to identify distinct metabolomic signatures in patients with PSVD, cirrhosis, and healthy volunteers (HV) to facilitate non-invasive diagnosis and elucidate perturbed metabolic pathways. MethodsSerum samples from 20 HV and patients with histologically confirmed PSVD or cirrhosis were analyzed. Metabolites were measured using liquid chromatography-mass spectrometry (LC-MS). Differential abundance was evaluated with Limma’s moderated t-statistics. Artificial neural network (ANN) and support vector machine (SVM) models were developed to classify PSVD against cirrhosis or HV metabolomic profiles. An independent cohort was used for validation. ResultsA total of 283 metabolites were included for downstream analysis. Clustering effectively separated PSVD metabolomes from HV, although a subset of PSVD patients (n=5, 25%) overlapped with cirrhosis. Differential testing revealed significant PSVD-linked metabolic perturbations, including taurocholic and adipic acids, distinguishing PSVD from both HV and cirrhosis. Alterations in pyrimidine, glycine, serine, and threonine pathways were exclusively associated with PSVD. Machine learning models utilizing selected metabolic signatures reliably differentiated PSVD from HV or cirrhosis using only 4 to 6 metabolites. Validation in an independent cohort demonstrated the high discriminative ability of taurocholic acid (AUROC 0.899) for PSVD vs HV and the taurocholic acid/aspartic acid ratio (AUROC 0.720) for PSVD vs cirrhosis. ConclusionsHigh-throughput metabolomics enabled the identification of distinct metabolic profiles for differentiating PSVD, cirrhosis, and healthy individuals. Unique alterations in the glycine, serine, and threonine pathways suggest their potential involvement in PSVD pathogenesis. Impact And ImplicationsPorto-sinusoidal vascular disorder (PSVD) is a vascular liver disease that can lead to pre-sinusoidal portal hypertension in the absence of cirrhosis, with poorly understood pathophysiology and no established treatment. Our study demonstrates that analyzing the serum metabolome could reveal distinct metabolic signatures in PSVD patients, including alterations in the pyrimidine, glycine, serine and threonine pathways and potentially shedding light on the disease's underlying pathways. These findings hold potential for earlier and non-invasive diagnosis of PSVD, potentially reducing reliance on invasive procedures like liver biopsy and guiding diagnostic pathways.

P21-31 Predictive metabolomic signatures for safety assessment of three plastic nanoparticles using intestinal organoids

P21-31 Predictive metabolomic signatures for safety assessment of three plastic nanoparticles using intestinal organoids

Effect of weight loss interventions on metabolomic signatures in obese children with insulin resistance

The obesity epidemic among children has become a major public health issue, and the presence of childhood insulin resistance (IR) has been demonstrated prior to the onset of type 2 diabetes mellitus. However, it is unclear whether the metabolomic signature is associated with weight loss interventions in obese children with IR. Thirty-six obese children with IR were selected from the weight loss camp (Shenzhen Sunshine Xing Yada health Technology Co., LTD). Clinical parameters were collected before and after weight loss intervention. Targeted metabolomics of plasma samples was performed by ultra-performance liquid chromatography coupled to the tandem mass spectrometry, and principal component analysis, variable importance in projection, and orthogonal partial least squares discriminant analysis were used to obtain the differentially expressed metabolites. Pathway analysis was conducted with the Homo sapiens (HSA) sets in the Kyoto Encyclopedia of Genes and Genomes. We used machine learning algorithms to obtain the potential biomarkers and Spearman correlation analysis to clarify the association between potential biomarkers and clinical parameters. We found that clinical parameters and metabolite clusters were significantly changed in obese children with IR before and after weight loss intervention. Mechanistically, weight loss intervention significantly changed 61 metabolites in obese children with IR. Furthermore, 12 pathways were significantly changed. Moreover, the machine learning algorithm found 6 important potential biomarkers. In addition, these potential biomarkers were strongly associated with major clinical parameters. These data indicate different metabolomic profiles in obese children with IR after weight loss intervention, providing insights into the clinical parameters and metabolite mechanisms involved in weight loss programs.

Metabolomic characterization of human glioblastomas and patient plasma: a pilot study

Background Glioblastoma (GBM) is a clinically challenging primary brain tumor with poor survival outcome despite surgical resection and intensive chemoradiation. The metabolic heterogeneity of GBM can become biomarkers for treatment response, resistance, and outcome prediction. The aim of the study is to investigate metabolic distinctions between primary and recurrent GBM tissue and patient plasma to establish feasibility for metabolic profiling. Methods A single-center cohort study analyzed tissue and blood samples from 15 patients with GBM using untargeted metabolomic/lipidomic assays. Metabolomic, lipidomic, and biogenic amine analyses were conducted on GBM tissue and patient plasma at diagnosis and recurrence using untargeted mass spectrometry. The study utilized a small but longitudinally collected cohort to evaluate alteration in metabolites, lipids, and biogenic amines between specimens at diagnosis and recurrence. Results Exploratory analysis revealed significant alteration in metabolites, lipids, and biogenic amines between diagnostic and recurrent states in both tumor and plasma specimens. Notable metabolites differed at recurrence, including N-alpha-methylhistamine, glycerol-3-phosphate, phosphocholine, and succinic acid in tissue, and indole-3-acetate, and urea in plasma. Principal component analysis revealed distinct metabolomic profiles between tumor tissue and patient plasma. Distinct metabolic profiles were observed in GBM tissue and patient plasma at recurrence, demonstrating the feasibility of using metabolomic methodologies for longitudinal studies. One patient exhibited a unique tumor resistance signature at diagnosis, possibly indicating a high-risk metabolomic phenotype. Conclusions In this small cohort, the findings suggest the potential of metabolomic signatures of GBM tissue and patient plasma for risk stratification, outcome prediction, and the development of novel adjuvant metabolic-targeting therapies. The findings suggest metabolic discrepancies at diagnosis and recurrence in tissue and plasma, highlighting potential implications for evaluation of clinical response. The identification of significant changes in metabolite abundance emphasizes the need for larger studies using targeted metabolomics to validate and further explore these profiles.