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Metabolomic-Based Approach to Identify Biomarkers of Apple Intake.
McNamara, AE, Collins, C, Harsha, PSCS, González-Peña, D, Gibbons, H, McNulty, BA, Nugent, AP, Walton, J, Flynn, A, Brennan, L
Molecular nutrition & food research. 2020;(11):e1901158
Abstract
SCOPE There is an increased interest in developing biomarkers of food intake to address some of the limitations associated with self-reported data. The objective is to identify biomarkers of apple intake, examine dose-response relationships, and agreement with self-reported data. METHODS AND RESULTS Metabolomic data from three studies are examined: an acute intervention, a short-term intervention, and a free-living cohort study. Fasting and postprandial urine samples are collected for analysis by 1 H-NMR and liquid chromatography-mass spectrometry (LC-MS). Calibration curves are developed to determine apple intake and classify individuals into categories of intake. Multivariate analysis of data reveals that levels of multiple metabolites increase significantly post-apple consumption, compared to the control food-broccoli. In the dose-response study, urinary xylose, epicatechin sulfate, and 2,6-dimethyl-2-(2-hydroxyethyl)-3,4-dihydro-2H-1-benzopyran increase as apple intake increases. Urinary xylose concentrations in a free-living cohort perform poorly at an individual level but are capable of ranking individuals in categories of intake. CONCLUSION Urinary xylose exhibits a dose-response relationship with apple intake and performs well as a ranking biomarker in the population study. Other potential biomarkers are identified and future work will combine these with xylose in a biomarker panel which may allow for a more objective determination of individual intake.
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Insulin Resistance in Obese Children: What Can Metabolomics and Adipokine Modelling Contribute?
Rupérez, FJ, Martos-Moreno, GÁ, Chamoso-Sánchez, D, Barbas, C, Argente, J
Nutrients. 2020;(11)
Abstract
The evolution of obesity and its resulting comorbidities differs depending upon the age of the subject. The dramatic rise in childhood obesity has resulted in specific needs in defining obesity-associated entities with this disease. Indeed, even the definition of obesity differs for pediatric patients from that employed in adults. Regardless of age, one of the earliest metabolic complications observed in obesity involves perturbations in glucose metabolism that can eventually lead to type 2 diabetes. In children, the incidence of type 2 diabetes is infrequent compared to that observed in adults, even with the same degree of obesity. In contrast, insulin resistance is reported to be frequently observed in children and adolescents with obesity. As this condition can be prerequisite to further metabolic complications, identification of biological markers as predictive risk factors would be of tremendous clinical utility. Analysis of obesity-induced modifications of the adipokine profile has been one classic approach in the identification of biomarkers. Recent studies emphasize the utility of metabolomics in the analysis of metabolic characteristics in children with obesity with or without insulin resistance. These studies have been performed with targeted or untargeted approaches, employing different methodologies. This review summarizes some of the advances in this field while emphasizing the importance of the different techniques employed.
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3.
Free-amino acid metabolic profiling of visceral adipose tissue from obese subjects.
Piro, MC, Tesauro, M, Lena, AM, Gentileschi, P, Sica, G, Rodia, G, Annicchiarico-Petruzzelli, M, Rovella, V, Cardillo, C, Melino, G, et al
Amino acids. 2020;(8):1125-1137
Abstract
Interest in adipose tissue pathophysiology and biochemistry have expanded considerably in the past two decades due to the ever increasing and alarming rates of global obesity and its critical outcome defined as metabolic syndrome (MS). This obesity-linked systemic dysfunction generates high risk factors of developing perilous diseases like type 2 diabetes, cardiovascular disease or cancer. Amino acids could play a crucial role in the pathophysiology of the MS onset. Focus of this study was to fully characterize amino acids metabolome modulations in visceral adipose tissues (VAT) from three adult cohorts: (i) obese patients (BMI 43-48) with metabolic syndrome (PO), (ii) obese subjects metabolically well (O), and (iii) non obese individuals (H). 128 metabolites identified as 20 protein amino acids, 85 related compounds and 13 dipeptides were measured by ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) and gas chromatography-/mass spectrometry GC/MS, in visceral fat samples from a total of 53 patients. Our analysis indicates a probable enhanced BCAA (leucine, isoleucine, valine) degradation in both VAT from O and PO subjects, while levels of their oxidation products are increased. Also PO and O VAT samples were characterized by: elevated levels of kynurenine, a catabolic product of tryptophan and precursor of diabetogenic substances, a significant increase of cysteine sulfinic acid levels, a decrease of 1-methylhistidine, and an up regulating trend of 3-methylhistidine levels. We hope this profiling can aid in novel clinical strategies development against the progression from obesity to metabolic syndrome.
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Analysis of biomarkers and metabolic pathways in patients with unstable angina based on ultra‑high‑performance liquid chromatography‑quadrupole time‑of‑flight mass spectrometry.
Liu, Y, Li, Y, Zhang, T, Zhao, H, Fan, S, Cai, X, Liu, Y, Li, Z, Gao, S, Li, Y, et al
Molecular medicine reports. 2020;(5):3862-3872
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Abstract
Unstable angina (UA) is a coronary disease with a high mortality and morbidity worldwide. The present study aimed to use non‑invasive techniques to identify urine biomarkers in patients with UA, so as to provide more information for the early diagnosis and treatment of the disease. Based on metabolomics, urine samples from 28 patients with UA and 28 healthy controls (HCs) were analyzed using ultra‑high‑performance liquid chromatography‑quadrupole time‑of‑flight mass spectrometry (UPLC‑Q‑TOF/MS). A total of 16 significant biomarkers that could distinguish between patients with UA and HCs, including D‑glucuronic acid, creatinine, succinic acid and N‑acetylneuraminic acid, were identified. The major metabolic pathways associated with UA were subsequently analyzed by non‑targeted metabolomics. The results demonstrated that amino acid and energy metabolism, fatty acid metabolism, purine metabolism and steroid hormone biosynthetic metabolism may serve important roles in UA. The results of the current study may provide a theoretical basis for the early diagnosis of UA and novel treatment strategies for clinicians. The trial was registered with the Chinese Clinical Trial Registration Center (registration no. ChiCTR‑ROC‑17013957) at Tianjin University of Traditional Chinese Medicine.
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Different metabolism of EPA, DPA and DHA in humans: A double-blind cross-over study.
Guo, XF, Tong, WF, Ruan, Y, Sinclair, AJ, Li, D
Prostaglandins, leukotrienes, and essential fatty acids. 2020;:102033
Abstract
This study aimed to compare eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA) and docosahexaenoic acid (DHA) incorporated into red blood cells (RBC) phospholipids (PL), plasma PL, plasma triglyceride (TAG), and plasma cholesteryl ester (CE) fractions, and the metabolomics profiles in a double-blind cross-over study. Twelve female healthy subjects randomly consumed 1 g per day for 6 days of pure EPA, DPA, or DHA. The placebo treatment was olive oil. The fasting venous blood was taken at days 0, 3 and 6, and the RBC PL and plasma lipid fractions were separated for fatty acid determination using thin layer chromatography followed by gas chromatography. Plasma metabolites were analyzed by UHPLC-Q-Exactive Orbitrap/MS. Supplemental EPA significantly increased the concentrations of EPA in RBC PL (days 3 and 6). For subjects consuming the DPA supplement, the concentrations of both DPA and EPA were significantly increased in RBC PL over a 6-day period, respectively. For plasma PL fraction, EPA and DPA supplementation significantly increased the concentrations of EPA and DPA at both days 3 and 6, respectively. Supplemental DHA significantly increased the concentrations of DHA in plasma PL at day 6. For plasma TAG fraction, supplementation with EPA and DPA significantly increased the concentrations of EPA and DPA at both days 3 and 6, respectively. After DHA supplementation, significant increases in the concentrations of DHA were found relative to baseline at both days 3 and 6. For plasma CE fraction, EPA supplementation significantly increased the concentrations of EPA (days 3 and 6) and DPA (days 6), respectively. Supplemental DPA significantly increased the concentrations of EPA at day 6. Meanwhile, the concentrations of DHA were significantly increased over a 6-day period of intervention after subjects consuming the DHA supplements. There were a total of 922 plasma metabolites identified using metabolomics analyses. Supplementation with DPA and DHA significantly increased the levels of sphingosine 1-phosphate (P for DPA = 0.025, P for DHA = 0.029) and 15-deoxy-Δ12,14-prostaglandin A1 (P for DPA = 0.034; P for DHA = 0.021) in comparison with olive oil group. Additionally, supplementation with EPA (P = 0.007) and DHA (P = 0.005) significantly reduced the levels of linoleyl carnitine, compared with olive oil group. This study shows that DPA might act as a reservoir of n-3 LCP incorporated into blood lipid fractions, metabolized into DHA, and retro-converted back to EPA. Metabolomics analyses indicate that supplemental EPA, DPA and DHA have shared and differentiated metabolites. The differences of these metabolic biomarkers should be investigated in additional studies.
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Do nuclear magnetic resonance (NMR)-based metabolomics improve the prediction of pregnancy-related disorders? Findings from a UK birth cohort with independent validation.
McBride, N, Yousefi, P, White, SL, Poston, L, Farrar, D, Sattar, N, Nelson, SM, Wright, J, Mason, D, Suderman, M, et al
BMC medicine. 2020;(1):366
Abstract
BACKGROUND Prediction of pregnancy-related disorders is usually done based on established and easily measured risk factors. Recent advances in metabolomics may provide earlier and more accurate prediction of women at risk of pregnancy-related disorders. METHODS We used data collected from women in the Born in Bradford (BiB; n = 8212) and UK Pregnancies Better Eating and Activity Trial (UPBEAT; n = 859) studies to create and validate prediction models for pregnancy-related disorders. These were gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), small for gestational age (SGA), large for gestational age (LGA) and preterm birth (PTB). We used ten-fold cross-validation and penalised regression to create prediction models. We compared the predictive performance of (1) risk factors (maternal age, pregnancy smoking, body mass index (BMI), ethnicity and parity) to (2) nuclear magnetic resonance-derived metabolites (N = 156 quantified metabolites, collected at 24-28 weeks gestation) and (3) combined risk factors and metabolites. The multi-ethnic BiB cohort was used for training and testing the models, with independent validation conducted in UPBEAT, a multi-ethnic study of obese pregnant women. RESULTS Maternal age, pregnancy smoking, BMI, ethnicity and parity were retained in the combined risk factor and metabolite models for all outcomes apart from PTB, which did not include maternal age. In addition, 147, 33, 96, 51 and 14 of the 156 metabolite traits were retained in the combined risk factor and metabolite model for GDM, HDP, SGA, LGA and PTB, respectively. These include cholesterol and triglycerides in very low-density lipoproteins (VLDL) in the models predicting GDM, HDP, SGA and LGA, and monounsaturated fatty acids (MUFA), ratios of MUFA to omega 3 fatty acids and total fatty acids, and a ratio of apolipoprotein B to apolipoprotein A-1 (APOA:APOB1) were retained predictors for GDM and LGA. In BiB, discrimination for GDM, HDP, LGA and SGA was improved in the combined risk factors and metabolites models. Risk factor area under the curve (AUC 95% confidence interval (CI)): GDM (0.69 (0.64, 0.73)), HDP (0.74 (0.70, 0.78)) and LGA (0.71 (0.66, 0.75)), and SGA (0.59 (0.56, 0.63)). Combined risk factor and metabolite models AUC 95% (CI): GDM (0.78 (0.74, 0.81)), HDP (0.76 (0.73, 0.79)) and LGA (0.75 (0.70, 0.79)), and SGA (0.66 (0.63, 0.70)). For GDM, HDP and LGA, but not SGA, calibration was good for a combined risk factor and metabolite model. Prediction of PTB was poor for all models. Independent validation in UPBEAT at 24-28 weeks and 15-18 weeks gestation confirmed similar patterns of results, but AUCs were attenuated. CONCLUSIONS Our results suggest a combined risk factor and metabolite model improves prediction of GDM, HDP and LGA, and SGA, when compared to risk factors alone. They also highlight the difficulty of predicting PTB, with all models performing poorly.
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7.
MMHub, a database for the mulberry metabolome.
Li, D, Ma, B, Xu, X, Chen, G, Li, T, He, N
Database : the journal of biological databases and curation. 2020
Abstract
Mulberry is an important economic crop plant and traditional medicine. It contains a huge array of bioactive metabolites such as flavonoids, amino acids, alkaloids and vitamins. Consequently, mulberry has received increasing attention in recent years. MMHub (version 1.0) is the first open public repository of mass spectra of small chemical compounds (<1000 Da) in mulberry leaves. The database contains 936 electrospray ionization tandem mass spectrometry (ESI-MS2) data and lists the specific distribution of compounds in 91 mulberry resources with two biological duplicates. ESI-MS2 data were obtained under non-standardized and independent experimental conditions. In total, 124 metabolites were identified or tentatively annotated and details of 90 metabolites with associated chemical structures have been deposited in the database. Supporting information such as PubChem compound information, molecular formula and metabolite classification are also provided in the MS2 spectral tag library. The MMHub provides important and comprehensive metabolome data for scientists working with mulberry. This information will be useful for the screening of quality resources and specific metabolites of mulberry. Database URL: https://biodb.swu.edu.cn/mmdb/.
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Untargeted Metabolomics Identifies Key Metabolic Pathways Altered by Thymoquinone in Leukemic Cancer Cells.
AlGhamdi, AA, Mohammed, MRS, Zamzami, MA, Al-Malki, AL, Qari, MH, Khan, MI, Choudhry, H
Nutrients. 2020;(6)
Abstract
Thymoquinone (TQ), a naturally occurring anticancer compound extracted from Nigella sativa oil, has been extensively reported to possess potent anti-cancer properties. Experimental studies showed the anti-proliferative, pro-apoptotic, and anti-metastatic effects of TQ on different cancer cells. One of the possible mechanisms underlying these effects includes alteration in key metabolic pathways that are critical for cancer cell survival. However, an extensive landscape of the metabolites altered by TQ in cancer cells remains elusive. Here, we performed an untargeted metabolomics study using leukemic cancer cell lines during treatment with TQ and found alteration in approximately 335 metabolites. Pathway analysis showed alteration in key metabolic pathways like TCA cycle, amino acid metabolism, sphingolipid metabolism and nucleotide metabolism, which are critical for leukemic cell survival and death. We found a dramatic increase in metabolites like thymine glycol in TQ-treated cancer cells, a metabolite known to induce DNA damage and apoptosis. Similarly, we observed a sharp decline in cellular guanine levels, important for leukemic cancer cell survival. Overall, we provided an extensive metabolic landscape of leukemic cancer cells and identified the key metabolites and pathways altered, which could be critical and responsible for the anti-proliferative function of TQ.
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Serum Metabolomic Response to Low- and High-Dose Vitamin E Supplementation in Two Randomized Controlled Trials.
Huang, J, Hodis, HN, Weinstein, SJ, Mack, WJ, Sampson, JN, Mondul, AM, Albanes, D
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2020;(7):1329-1334
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Abstract
BACKGROUND Vitamin E is an essential micronutrient and critical human antioxidant previously tested for cancer preventative effects with conflicting clinical trial results that have yet to be explained biologically. METHODS We examined baseline and on-trial serum samples for 154 men randomly assigned to receive 400 IU vitamin E (as alpha-tocopheryl acetate; ATA) or placebo daily in the Vitamin E Atherosclerosis Prevention Study (VEAPS), and for 100 men administered 50 IU ATA or placebo daily in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (ATBC). Over 970 metabolites were identified using ultrahigh-performance LC/MS-MS. Linear regression models estimated the change in serum metabolites of men supplemented with vitamin E versus those receiving placebo in VEAPS as compared with ATBC. RESULTS Serum alpha-carboxyethyl hydrochroman (CEHC) sulfate, alpha-tocopherol, and beta/gamma-tocopherol were significantly altered by ATA supplementation in both trials (all P values ≤5.1 × 10-5, the Bonferroni multiple comparisons corrected statistical threshold). Serum C22 lactone sulfate was significantly decreased in response to the high-dose vitamin E in VEAPS (β = -0.70, P = 8.1 × 10-6), but not altered by the low dose in ATBC (β = -0.17, P = 0.4). In addition, changes in androgenic steroid metabolites were strongly correlated with the vitamin E supplement-associated change in C22 lactone sulfate only in the VEAPS trial. CONCLUSIONS We found evidence of a dose-dependent vitamin E supplementation effect on a novel C22 lactone sulfate compound that was correlated with several androgenic steroids. IMPACT Our data add information on a differential hormonal response based on vitamin E dose that could have direct relevance to opposing prostate cancer incidence results from previous large controlled trials.
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A Multi-Cohort Metabolomics Analysis Discloses Sphingomyelin (32:1) Levels to be Inversely Related to Incident Ischemic Stroke.
Lind, L, Salihovic, S, Ganna, A, Sundström, J, Broeckling, CD, Magnusson, PK, Pedersen, NL, Siegbahn, A, Prenni, J, Fall, T, et al
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association. 2020;(2):104476
Abstract
BACKGROUND AND PURPOSE To search for novel pathophysiological pathways related to ischemic stroke using a metabolomics approach. METHODS We identified 204 metabolites in plasma by liquid chromatography mass spectrometry in 3 independent population-based samples (TwinGene, Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) and Uppsala Longitudinal Study of Adult Men). TwinGene was used for discovery and the other 2 samples were meta-analyzed as replication. In PIVUS, traditional cardiovascular (CV) risk factors, multiple markers of subclinical CV disease, markers of coagulation/fibrinolysis were measured and analyzed in relation to top metabolites. RESULTS In TwinGene (177 incident cases, median follow-up 4.3 years), levels of 28 metabolites were associated with incident ischemic stroke at a false discover rate (FDR) of 5%. In the replication (together 194 incident cases, follow-up 10 and 12 years, respectively), only sphingomyelin (32:1) was significantly associated (HR .69 per SD change, 95% CI .57-0.83, P value = .00014; FDR <5%) when adjusted for systolic blood pressure, diabetes, smoking, low density lipoportein (LDL)- and high density lipoprotein (HDL), body mass index (BMI) and atrial fibrillation. In PIVUS, sphingomyelin (32:1) levels were significantly related to both LDL- and HDL-cholesterol in a positive fashion, and to serum triglycerides, BMI and diabetes in a negative fashion. Furthermore, sphingomyelin (32:1) levels were related to vasodilation in the forearm resistance vessels, and inversely to leukocyte count (P < .0069 and .0026, respectively). CONCLUSIONS An inverse relationship between sphingomyelin (32:1) and incident ischemic stroke was identified, replicated, and characterized. A possible protective role for sphingomyelins in stroke development has to be further investigated in additional experimental and clinical studies.