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Introduction: The lens's metabolic demands are met through a continuous circulation of aqueous humor, encompassing a spectrum of components such as organic and inorganic ions, carbohydrates, glutathione, urea, amino acids, proteins, oxygen, carbon dioxide, and water. Metabolomics is a pivotal tool, offering an initial insight into the complexities of integrated metabolism. In this investigative study, we systematically scrutinize the composition of intraocular fluid in individuals afflicted with cataracts. Methods: The investigation involved a comprehensive analysis of aqueous humor samples from a cohort comprising 192 patients. These individuals were stratified by utilizing the SPONCS classification system, delineating distinct groups characterized by the hardness of cataracts. The analytical approach employed targeted quantitative metabolite analysis using HILIC-based liquid chromatography coupled with high-resolution mass spectrometric detection. The metabolomics data analysis was performed with MetaboAnalyst 5.0. Results: The results of the enrichment analysis have facilitated the inference that the discerned disparities among groups arise from disruptions in taurine and hypotaurine metabolism, variations in tryptophan metabolism, and modifications in mitochondrial beta-oxidation of short-chain saturated fatty acids and pyrimidine metabolism. Conclusion: A decline in taurine concentration precipitates diminished glutathione activity, prompting an elevated requirement for NAD+ and instigating tryptophan metabolism along the kynurenine pathway. Activation of this pathway is additionally prompted by interferon-gamma and UV radiation, leading to the induction of IDO. Concurrently, heightened mitochondrial beta-oxidation signifies a distinctive scenario in translocating fatty acids into the mitochondria, enhancing energy production.

Introduction: Hereditary angioedema (HAE) is a rare, life-threatening autosomal dominant genetic disorder caused by a deficient and/or dysfunctional C1 esterase inhibitor (C1-INH) (type 1 and type 2) leading to recurrent episodes of edema. This study aims to explore HAE patients' metabolomic profiles and identify novel potential diagnostic biomarkers for HAE. The study also examined distinguishing HAE from idiopathic angioedema (AE)., Methods: Blood plasma samples from 10 HAE (types 1/2) patients, 15 patients with idiopathic AE, and 20 healthy controls were collected in Latvia and analyzed using LC-MS based targeted metabolomics workflow. T-test and fold change calculation were used to identify metabolites with significant differences between diseases and control groups. ROC analysis was performed to evaluate metabolite based classification model., Results: A total of 33 metabolites were detected and quantified. The results showed that isovalerylcarnitine, cystine, and hydroxyproline were the most significantly altered metabolites between the disease and control groups. Aspartic acid was identified as a significant metabolite that could differentiate between HAE and idiopathic AE. The mathematical combination of metabolites (hydroxyproline * cystine)/(creatinine * isovalerylcarnitine) was identified as the diagnosis signature for HAE. Furthermore, glycine/asparagine ratio could differentiate between HAE and idiopathic AE., Conclusion: Our study identified isovalerylcarnitine, cystine, and hydroxyproline as potential biomarkers for HAE diagnosis. Identifying new biomarkers may offer enhanced prospects for accurate, timely, and economical diagnosis of HAE, as well as tailored treatment selection for optimal patient care., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Kanepa, Fan, Rots, Vaska, Ansone, Briviba, Klovins, Kurjane and Klavins.)

Background and Aims: Preclinical experiments on animal models are essential to understand the mechanisms of cardiovascular disease (CVD). Metabolomics allows access to the metabolic perturbations associated with CVD in heart and vessels. Here we assessed which potential animal CVD model most closely mimics the serum metabolite signature of increased carotid intima-media thickness (cIMT) in humans, a clinical parameter widely accepted as a surrogate of CVD., Methods: A targeted mass spectrometry assay was used to quantify and compare a series of blood metabolites between 1362 individuals (KORA F4 cohort) and 5 animal CVD models: ApoE - / - , Ldlr - / - , and klotho-hypomorphic mice (kl/kl) and SHRSP rats with or without salt feeding. The metabolite signatures were obtained using linear regressions adjusted for various co-variates., Results: In human, increased cIMT [quartile Q4 vs. Q1] was associated with 26 metabolites (9 acylcarnitines, 2 lysophosphatidylcholines, 9 phosphatidylcholines and 6 sphingomyelins). Acylcarnitines correlated preferentially with serum glucose and creatinine. Phospholipids correlated preferentially with cholesterol (total and LDL). The human signature correlated positively and significantly with Ldlr - / - and ApoE - / - mice, while correlation with kl/kl mice and SHRP rats was either negative and non-significant. Human and Ldlr - / - mice shared 11 significant metabolites displaying the same direction of regulation: 5 phosphatidylcholines, 1 lysophosphatidylcholines, 5 sphingomyelins; ApoE - / - mice shared 10., Conclusions: The human cIMT signature was partially mimicked by Ldlr - / - and ApoE - / - mice. These animal models might help better understand the biochemical and molecular mechanisms involved in the vessel metabolic perturbations associated with, and contributing to metabolic disorders in CVD., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Alzheimer's disease (AD) is the most common neurodegenerative disease presenting major health and economic challenges that continue to grow. Mechanisms of disease are poorly understood but significant data point to metabolic defects that might contribute to disease pathogenesis. The Alzheimer Disease Metabolomics Consortium (ADMC) in partnership with Alzheimer Disease Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for AD. Using targeted and non- targeted metabolomics and lipidomics platforms we are mapping metabolic pathway and network failures across the trajectory of disease. In this report we present quantitative metabolomics data generated on serum from 199 control, 356 mild cognitive impairment and 175 AD subjects enrolled in ADNI1 using AbsoluteIDQ-p180 platform, along with the pipeline for data preprocessing and medication classification for confound correction. The dataset presented here is the first of eight metabolomics datasets being generated for broad biochemical investigation of the AD metabolome. We expect that these collective metabolomics datasets will provide valuable resources for researchers to identify novel molecular mechanisms contributing to AD pathogenesis and disease phenotypes.

Introduction: Recently, quantitative metabolomics identified a panel of 10 plasma lipids that were highly predictive of conversion to Alzheimer's disease (AD) in cognitively normal older individuals (n = 28, area under the curve [AUC] = 0.92, sensitivity/specificity of 90%/90%)., Methods: Quantitative targeted metabolomics in serum using an identical method as in the index study., Results: We failed to replicate these findings in a substantially larger study from two independent cohorts-the Baltimore Longitudinal Study of Aging ([BLSA], n = 93, AUC = 0.642, sensitivity/specificity of 51.6%/65.7%) and the Age, Gene/Environment Susceptibility-Reykjavik Study ([AGES-RS], n = 100, AUC = 0.395, sensitivity/specificity of 47.0%/36.0%). In analyses applying machine learning methods to all 187 metabolite concentrations assayed, we find a modest signal in the BLSA with distinct metabolites associated with the preclinical and symptomatic stages of AD, whereas the same methods gave poor classification accuracies in the AGES-RS samples., Discussion: We believe that ours is the largest blood biomarker study of preclinical AD to date. These findings underscore the importance of large-scale independent validation of index findings from biomarker studies with relatively small sample sizes., (Published by Elsevier Inc.)

Quantitative metabolic profiling is preceded by dedicated sample preparation protocols. These multistep procedures require detailed optimization and thorough validation. In this work, a uniformly (13)C-labeled (U(13)C) cell extract was used as a tool to evaluate the recoveries and repeatability precisions of the cell extraction and the extract treatment. A homogenous set of biological replicates (n = 15 samples of Pichia pastoris) was prepared for these fundamental experiments. A range of less than 30 intracellular metabolites, comprising amino acids, nucleotides, and organic acids were measured both in monoisotopic (12)C and U(13)C form by LC-MS/MS employing triple quadrupole MS, reversed phase chromatography, and HILIC. Recoveries of the sample preparation procedure ranging from 60 to 100% and repeatability precisions below 10% were obtained for most of the investigated metabolites using internal standardization approaches. Uncertainty budget calculations revealed that for this complex quantification task, in the optimum case, total combined uncertainty of 12% could be achieved. The optimum case would be represented by metabolites, easy to extract from yeast with high and precise recovery. In other cases the total combined uncertainty was significantly higher., (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)