A Comparison of Dry Bean and Pea Consumption on Serum Cholesterol: A Randomized Controlled Trial in Adults with Mild Hypercholesterolemia.

BACKGROUND: Diets including pulses are associated with better cardiovascular profiles, including lipid, glycemia, and hemodynamics; however, evidence is lacking regarding the contributions of individual pulse varieties. OBJECTIVES: This randomized, controlled trial examined the effects of beans or peas individually, relative to rice, on LDL-cholesterol levels (primary outcome) and other indices of cardiovascular disease risk (secondary outcomes) at 6 wk in adults with mild hypercholesterolemia. METHODS: This randomized, controlled, single-blind, 3-arm parallel-group study was conducted in 2 Canadian cities (Edmonton, Alberta; Winnipeg, Manitoba). Participants (n = 60 per group) were randomly assigned to 6 wk of regular consumption of foods containing either 120 g (∼0.75 cups) of beans (mixture of black, great northern, navy, and pinto) or 120 g (∼0.75 cups) peas (mixture of yellow and green), or identical foods containing white, parboiled rice (control foods). LDL-cholesterol (primary outcome) and indices of lipid metabolism, glycemia, and hemodynamics (secondary outcomes) were assessed. RESULTS: Mean LDL-cholesterol was lower in the bean group (-0.21; 95% CI: -0.39, -0.03) but not the pea group (-0.11; 95% CI: -0.29, 0.07) relative to rice after 6 wk. Non-HDL-cholesterol (-0.20; 95% CI: -0.40, -0.002) and total cholesterol (-0.28; 95% CI: -0.49, -0.06) were also lower in the bean compared with rice groups. No changes were noted in triglycerides (-0.07; 95% CI: -0.28, 0.14), glucose (0.02; 95% CI: -0.17, 0.14), insulin (4.94; 95% CI: -5.51, 11.38), or blood pressure (systolic: -1.39; 95% CI: -5.18, 2.40; diastolic: -1.89; 95% CI: -4.65, 0.88). Dietary fiber intake (grams per day or grams per 1000 kcal) was not correlated with LDL-cholesterol (grams per day: r2 = 0.209, P = 0.142; grams per 1000 kcal: r2 =0.126, P = 0.379) in the bean group. Gastrointestinal effects were transient and most often not related to the study foods. CONCLUSIONS: Beans, but not peas, lowered LDL-cholesterol, relative to rice, in adults with mild hypercholesterolemia. Fiber may not be responsible for the effect of beans, suggesting other phytochemicals may be the active component(s). Strategies incorporating 120 g of pulses in a meal are feasible for managing some cardiometabolic risk factors. This trial was registered at clinicaltrials.gov as NCT01661543.

Empirically establishing drug exposure records directly from untargeted metabolomics data.

Despite extensive efforts, extracting information on medication exposure from clinical records remains challenging. To complement this approach, we developed the tandem mass spectrometry (MS/MS) based GNPS Drug Library. This resource integrates MS/MS data for drugs and their metabolites/analogs with controlled vocabularies on exposure sources, pharmacologic classes, therapeutic indications, and mechanisms of action. It enables direct analysis of drug exposure and metabolism from untargeted metabolomics data independent of clinical records. Our library facilitates stratification of individuals in clinical studies based on the empirically detected medications, exemplified by drug-dependent microbiota-derived N-acyl lipid changes in a cohort with human immunodeficiency virus. The GNPS Drug Library holds potential for broader applications in drug discovery and precision medicine.

Metabolomic Profiling of Pulmonary Neuroendocrine Neoplasms.

BACKGROUND/OBJECTIVES: Pulmonary neuroendocrine neoplasms (NENs) account for 20% of malignant lung tumors. Their management is challenging due to their diverse clinical features and aggressive nature. Currently, metabolomics offers a range of potential cancer biomarkers for diagnosis, monitoring tumor progression, and assessing therapeutic response. However, a specific metabolomic profile for early diagnosis of lung NENs has yet to be identified. This study aims to identify specific metabolomic profiles that can serve as biomarkers for early diagnosis of lung NENs. METHODS: We measured 153 metabolites using liquid chromatography combined with mass spectrometry (LC-MS) in the plasma of 120 NEN patients and compared them with those of 71 healthy individuals. Additionally, we compared these profiles with those of 466 patients with non-small-cell lung cancers (NSCLCs) to ensure clinical relevance. RESULTS: We identified 21 metabolites with consistently altered plasma concentrations in NENs. Compared to healthy controls, 18 metabolites were specific to carcinoid tumors, 5 to small-cell lung carcinomas (SCLCs), and 10 to large-cell neuroendocrine carcinomas (LCNECs). These findings revealed alterations in various metabolic pathways, such as fatty acid biosynthesis and beta-oxidation, the Warburg effect, and the citric acid cycle. CONCLUSIONS: Our study identified biomarker metabolites in the plasma of patients with each subtype of lung NENs and demonstrated significant alterations in several metabolic pathways. These metabolomic profiles could potentially serve as biomarkers for early diagnosis and better management of lung NENs.

An Overview of Pre-Analytical Factors Impacting Metabolomics Analyses of Blood Samples.

Discrepant sample processing remains a significant challenge within blood metabolomics research, introducing non-biological variation into the measured metabolome and biasing downstream results. Inconsistency during the pre-analytical phase can influence experimental processes, producing metabolome measurements that are non-representative of in vivo composition. To minimize variation, there is a need to create and adhere to standardized pre-analytical protocols for blood samples intended for use in metabolomics analyses. This will allow for reliable and reproducible findings within blood metabolomics research. In this review article, we provide an overview of the existing literature pertaining to pre-analytical factors that influence blood metabolite measurements. Pre-analytical factors including blood tube selection, pre- and post-processing time and temperature conditions, centrifugation conditions, freeze-thaw cycles, and long-term storage conditions are specifically discussed, with recommendations provided for best practices at each stage.

Introducing 'identification probability' for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies.

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence - the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in context of the chemical space being considered, are easily automated, or are transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a reference library or chemical space that match to an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multi-property reference libraries constructed from the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

Multiomics: Functional Molecular Biomarkers of Micronutrients for Public Health Application.

Adequate micronutrient intake and status are global public health goals. Vitamin and mineral deficiencies are widespread and known to impair health and survival across the life stages. However, knowledge of molecular effects, metabolic pathways, biological responses to variation in micronutrient nutriture, and abilities to assess populations for micronutrient deficiencies and their pathology remain lacking. Rapidly evolving methodological capabilities in genomics, epigenomics, proteomics, and metabolomics offer unparalleled opportunities for the nutrition research community to link micronutrient exposure to cellular health; discover new, arguably essential micronutrients of microbial origin; and integrate methods of molecular biology, epidemiology, and intervention trials to develop novel approaches to assess and prevent micronutrient deficiencies in populations. In this review article, we offer new terminology to specify nutritional application of multiomic approaches and encourage collaboration across the basic to public health sciences to advance micronutrient deficiency prevention.

Automatic Chemical Profiling of Wine by Proton Nuclear Magnetic Resonance Spectroscopy.

We report the development of MagMet-W (magnetic resonance for metabolomics of wine), a software program that can automatically determine the chemical composition of wine via 1H nuclear magnetic resonance (NMR) spectroscopy. MagMet-W is an extension of MagMet developed for the automated metabolomic analysis of human serum by 1H NMR. We identified 70 compounds suitable for inclusion into MagMet-W. We then obtained 1D 1H NMR reference spectra of the pure compounds at 700 MHz and incorporated these spectra into the MagMet-W compound library. The processing of the wine NMR spectra and profiling of the 70 wine compounds were then optimized based on manual 1H NMR analysis. MagMet-W can automatically identify 70 wine compounds in most wine samples and can quantify them to 10-15% of the manually determined concentrations, and it can analyze multiple spectra simultaneously, at 10 min per spectrum. The MagMet-W Web server is available at https://www.magmet.ca.

The Open SprayBot: A high-throughput paper spray mass spectrometry platform for disease screening.

Newborn disease screening increases survival, improves quality of life and reduces treatment costs for healthcare systems. Mass spectrometry (MS) is an effective method for metabolic screening. However, conventional analytical methods require biofluid handling and cooling conditions during transport, making the logistics difficult and expensive, especially for remote regions. 'Paper-spray' (PS) ionization generates a charged solvent spray from samples deposited on paper strips. Therefore, samples can be applied on a suitable matrix and shipped as dried spots to diagnostic laboratories with standard postal or messenger services. We built a robotic platform, the 'Open SprayBot', to automatically analyze paper-deposited samples via PS-MS and increase the sample throughput. The system is operated via RUMBA32 and Arduino Mega boards. A commercial syringe pump and power supply provide solvent application and electrical current required for PS-MS. The usability of the Open SprayBot was demonstrated by quantifying palmitoyl-l-carnitine, a common biomarker in newborn screening.

Knowledge translation and knowledge mobilization from the FoodBAll project.

This report describes the knowledge mobilization and translation outcomes of the Canadian-funded portion of a large, international project called the Food Biomarker Alliance (FoodBAll), which ran from 2015 to 2019. This remarkably successful project led to a large number of important findings, outputs, and impacts. In particular, FoodBAll unequivocally demonstrated that metabolomics could be used to not only discover biomarkers of food intake (BFIs), but also to measure diet in a more objective manner. FoodBAll also created standards for assessing and validating BFIs, papers and databases describing BFIs, and kits for measuring BFIs and it laid the groundwork for many global studies exploring food composition and precision nutrition.

Plasma taurine level is linked to symptom burden and clinical outcomes in post-COVID condition.

BACKGROUND: A subset of individuals (10-20%) experience post-COVID condition (PCC) subsequent to initial SARS-CoV-2 infection, which lacks effective treatment. PCC carries a substantial global burden associated with negative economic and health impacts. This study aims to evaluate the association between plasma taurine levels with self-reported symptoms and adverse clinical outcomes in patients with PCC. METHODS AND FINDINGS: We analyzed the plasma proteome and metabolome of 117 individuals during their acute COVID-19 hospitalization and at the convalescence phase six-month post infection. Findings were compared with 28 age and sex-matched healthy controls. Plasma taurine levels were negatively associated with PCC symptoms and correlated with markers of inflammation, tryptophan metabolism, and gut dysbiosis. Stratifying patients based on the trajectories of plasma taurine levels during six-month follow-up revealed a significant association with adverse clinical events. Increase in taurine levels during the transition to convalescence were associated with a reduction in adverse events independent of comorbidities and acute COVID-19 severity. In a multivariate analysis, increased plasma taurine level between acute and convalescence phase was associated with marked protection from adverse clinical events with a hazard ratio of 0.13 (95% CI: 0.05-0.35; p<0.001). CONCLUSIONS: Taurine emerges as a promising predictive biomarker and potential therapeutic target in PCC. Taurine supplementation has already demonstrated clinical benefits in various diseases and warrants exploration in large-scale clinical trials for alleviating PCC.

Serum Metabolites Relate to Mucosal and Transmural Inflammation in Pediatric Crohn Disease.

BACKGROUND AND AIMS: We aimed to identify serum metabolites associated with mucosal and transmural inflammation in pediatric Crohn disease (pCD). METHODS: Fifty-six pCD patients were included through a pre-planned sub-study of the multicenter, prospective, ImageKids cohort, designed to develop the Pediatric Inflammatory Crohn's MRE Index (PICMI). Children were included throughout their disease course when undergoing ileocolonoscopy and magnetic resonance enterography (MRE) and followed for 18 months when MRE was repeated. Serum metabolites were identified using liquid chromatography/mass spectroscopy. Outcomes included: PICMI, the simple endoscopic score (SES), faecal calprotectin (FCP), and C-reactive protein (CRP), to assess transmural, mucosal, and systemic inflammation, respectively. Random forest models were built by outcome. Maximum relevance minimum redundancy (mRMR) feature selection with a j-fold cross validation scheme identified the best subset of features and hyperparameter settings. RESULTS: Tryptophan and glutarylcarnitine were the top common mRMR metabolites linked to pCD inflammation. Random forest models established that amino acids and amines were among the most influential metabolites for predicting transmural and mucosal inflammation. Predictive models performed well, each with an area under the curve (AUC) > 70%. In addition, serum metabolites linked with pCD inflammation mainly related to perturbations in citrate cycle (TCA cycle), aminoacyl-tRNA biosynthesis, tryptophan metabolism, butanoate metabolism, and tyrosine metabolism. CONCLUSIONS: We extend on recent studies, observing differences in serum metabolite between healthy controls and Crohn disease patients, and suggest various associations of serum metabolites with transmural and mucosal inflammation. These metabolites could improve the understanding of pCD pathogenesis and assess disease severity.

Redox-Modified Nanostructured Electrochemical Surfaces for Continuous Glucose Monitoring in Complex Biological Fluids.

Continuous glucose monitoring is valuable for people with diabetes but faces limitations due to enzyme-electrode interactions and biofouling from biological samples that reduce sensor sensitivity and the monitoring performance. We created an enzyme-based electrochemical system with a unique nanocomposite coating that incorporates the redox molecule, aminoferrocene (NH2-Fc). This coating enhances stability via electroactivity and reduces nonspecific binding, as demonstrated through cyclic voltammetry. Our approach enables real-time glucose detection via chronoamperometry with a calculated linear range of 0.5 to 20 mM and a 1 mM detection limit. Validated with plasma and saliva, this platform shows promise for robust metabolite detection in clinical and research contexts. This versatile platform can be applied to accurately monitor a wide range of metabolites in various biological matrices, improving patient outcomes.

GCMS-ID: a webserver for identifying compounds from gas chromatography mass spectrometry experiments.

GCMS-ID (Gas Chromatography Mass Spectrometry compound IDentifier) is a webserver designed to enable the identification of compounds from GC-MS experiments. GC-MS instruments produce both electron impact mass spectra (EI-MS) and retention index (RI) data for as few as one, to as many as hundreds of different compounds. Matching the measured EI-MS, RI or EI-MS + RI data to experimentally collected EI-MS and/or RI reference libraries allows facile compound identification. However, the number of available experimental RI and EI-MS reference spectra, especially for metabolomics or exposomics-related studies, is disappointingly small. Using machine learning to accurately predict the EI-MS spectra and/or RIs for millions of metabolomics and/or exposomics-relevant compounds could (partially) solve this spectral matching problem. This computational approach to compound identification is called in silico metabolomics. GCMS-ID brings this concept of in silico metabolomics closer to reality by intelligently integrating two of our previously published webservers: CFM-EI and RIpred. CFM-EI is an EI-MS spectral prediction webserver, and RIpred is a Kovats RI prediction webserver. We have found that GCMS-ID can accurately identify compounds from experimental RI, EI-MS or RI + EI-MS data through matching to its own large library of >1 million predicted RI/EI-MS values generated for metabolomics/exposomics-relevant compounds. GCMS-ID can also predict the RI or EI-MS spectrum from a user-submitted structure or annotate a user-submitted EI-MS spectrum. GCMS-ID is freely available at https://gcms-id.ca/.

Year-Long Stability of Nucleic Acid Bases in Concentrated Sulfuric Acid: Implications for the Persistence of Organic Chemistry in Venus' Clouds.

We show that the nucleic acid bases adenine, cytosine, guanine, thymine, and uracil, as well as 2,6-diaminopurine, and the "core" nucleic acid bases purine and pyrimidine, are stable for more than one year in concentrated sulfuric acid at room temperature and at acid concentrations relevant for Venus clouds (81% w/w to 98% w/w acid, the rest water). This work builds on our initial stability studies and is the first ever to test the reactivity and structural integrity of organic molecules subjected to extended incubation in concentrated sulfuric acid. The one-year-long stability of nucleic acid bases supports the notion that the Venus cloud environment-composed of concentrated sulfuric acid-may be able to support complex organic chemicals for extended periods of time.

Accurate Prediction of 1H NMR Chemical Shifts of Small Molecules Using Machine Learning.

NMR is widely considered the gold standard for organic compound structure determination. As such, NMR is routinely used in organic compound identification, drug metabolite characterization, natural product discovery, and the deconvolution of metabolite mixtures in biofluids (metabolomics and exposomics). In many cases, compound identification by NMR is achieved by matching measured NMR spectra to experimentally collected NMR spectral reference libraries. Unfortunately, the number of available experimental NMR reference spectra, especially for metabolomics, medical diagnostics, or drug-related studies, is quite small. This experimental gap could be filled by predicting NMR chemical shifts for known compounds using computational methods such as machine learning (ML). Here, we describe how a deep learning algorithm that is trained on a high-quality, "solvent-aware" experimental dataset can be used to predict 1H chemical shifts more accurately than any other known method. The new program, called PROSPRE (PROton Shift PREdictor) can accurately (mean absolute error of <0.10 ppm) predict 1H chemical shifts in water (at neutral pH), chloroform, dimethyl sulfoxide, and methanol from a user-submitted chemical structure. PROSPRE (pronounced "prosper") has also been used to predict 1H chemical shifts for >600,000 molecules in many popular metabolomic, drug, and natural product databases.

Blood-Based Multiomics-Guided Detection of a Precancerous Pancreatic Tumor.

Over a decade ago, longitudinal multiomics analysis was pioneered for early disease detection and individually tailored precision health interventions. However, high sample processing costs, expansive multiomics measurements along with complex data analysis have made this approach to precision/personalized medicine impractical. Here we describe in a case report, a more practical approach that uses fewer measurements, annual sampling, and faster decision making. We also show how this approach offers promise to detect an exceedingly rare and potentially fatal condition before it fully manifests. Specifically, we describe in the present case report how longitudinal multiomics monitoring (LMOM) helped detect a precancerous pancreatic tumor and led to a successful surgical intervention. The patient, enrolled in an annual blood-based LMOM since 2018, had dramatic changes in the June 2021 and 2022 annual metabolomics and proteomics results that prompted further clinical diagnostic testing for pancreatic cancer. Using abdominal magnetic resonance imaging, a 2.6 cm lesion in the tail of the patient's pancreas was detected. The tumor fluid from an aspiration biopsy had 10,000 times that of normal carcinoembryonic antigen levels. After the tumor was surgically resected, histopathological findings confirmed it was a precancerous pancreatic tumor. Postoperative omics testing indicated that most metabolite and protein levels returned to patient's 2018 levels. This case report illustrates the potentials of blood LMOM for precision/personalized medicine, and new ways of thinking medical innovation for a potentially life-saving early diagnosis of pancreatic cancer. Blood LMOM warrants future programmatic translational research with the goals of precision medicine, and individually tailored cancer diagnoses and treatments.

Protocol to identify biomarkers in patients with post-COVID condition using multi-omics and machine learning analysis of human plasma.

Here, we present a workflow for analyzing multi-omics data of plasma samples in patients with post-COVID condition (PCC). Applicable to various diseases, we outline steps for data preprocessing and integrating diverse assay datasets. Then, we detail statistical analysis to unveil plasma profile changes and identify biomarker-clinical variable associations. The last two steps discuss machine learning techniques for unsupervised clustering of patients based on their inherent molecular similarities and feature selection to identify predictive biomarkers. For complete details on the use and execution of this protocol, please refer to Wang et al.1.

Identifying Predictive Biomarkers of Subclinical Mastitis in Dairy Cows through Urinary Metabotyping.

Mastitis is a significant infectious disease in dairy cows, resulting in milk yield loss and culling. Early detection of mastitis-prone cows is crucial for implementing effective preventive measures before disease onset. Current diagnosis of subclinical mastitis (SCM) relies on somatic cell count assessment post-calving, lacking predictive capabilities. This study aimed to identify metabolic changes in pre-SCM cows through targeted metabolomic analysis of urine samples collected 8 wks and 4 wks before calving, using mass spectrometry. A nested case-control design was employed, involving a total of 145 multiparous dairy cows, with disease occurrence monitored pre- and postpartum. Among them, 15 disease-free cows served as healthy controls (CON), while 10 cows exclusively had SCM, excluding those with additional diseases. Urinary metabolite profiling revealed multiple alterations in acylcarnitines, amino acids, and organic acids in pre-SCM cows. Metabotyping identified 27 metabolites that distinguished pre-SCM cows from healthy CON cows at both 8 and 4 wks before parturition. However, only four metabolites per week showed significant alterations (p < 0.005). Notably, a panel of four serum metabolites (asymmetric dimethylarginine, proline, leucine, and homovanillate) at 8 wks prepartum, and another panel (asymmetric dimethylarginine, methylmalonate, citrate, and spermidine) at 4 wks prepartum, demonstrated predictive ability as urinary biomarkers for SCM risk (AUC = 0.88; p = 0.02 and AUC = 0.88; p = 0.03, respectively). In conclusion, our findings indicate that metabolite testing can identify cows at risk of SCM as early as 8 and 4 wks before parturition. Validation of the two identified metabolite panels is warranted to implement these predictive biomarkers, facilitate early intervention strategies, and improve dairy cow management to mitigate the impact of SCM. Further research is needed to confirm the efficacy and applicability of these biomarkers in practical farm settings.

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation.

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

Development of a point-of-care colorimetric metabolomic sensor platform.

Metabolomics is the large-scale study of small molecule metabolites within a biological system. It has applications in measuring dietary intake, predicting heart disease risk, and diagnosing cancer. Metabolites are often measured using high-end analytical tools such as mass spectrometers or large spectrophotometers. However, due to their size, cost, and need for skilled operators, using such equipment at the bedside is not practical. To address this issue, we have developed a low-cost, portable, optical color sensor platform for metabolite detection. This platform includes LEDs, sensors, microcontrollers, a power source, and a Bluetooth chip enclosed within a 3D-printed light-tight case. We evaluated the color sensor's performance using both a range of dyed water samples as well as well-established colorimetric reactions for specific metabolite detection. The sensor accurately measured creatinine, L-carnitine, ascorbate, and succinate well within normal human urine levels with accuracy and sensitivity equal to or better than a standard laboratory spectrophotometer. Our color sensor offers a cost-effective, portable alternative for measuring metabolites via colorimetric assays, thereby enabling low-cost, point-of-care metabolite testing.