-
1.
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.
-
2.
Urine steroid metabolomics for the differential diagnosis of adrenal incidentalomas in the EURINE-ACT study: a prospective test validation study.
Bancos, I, Taylor, AE, Chortis, V, Sitch, AJ, Jenkinson, C, Davidge-Pitts, CJ, Lang, K, Tsagarakis, S, Macech, M, Riester, A, et al
The lancet. Diabetes & endocrinology. 2020;(9):773-781
Abstract
BACKGROUND Cross-sectional imaging regularly results in incidental discovery of adrenal tumours, requiring exclusion of adrenocortical carcinoma (ACC). However, differentiation is hampered by poor specificity of imaging characteristics. We aimed to validate a urine steroid metabolomics approach, using steroid profiling as the diagnostic basis for ACC. METHODS We did a prospective multicentre study in adult participants (age ≥18 years) with newly diagnosed adrenal masses. We assessed the accuracy of diagnostic imaging strategies based on maximum tumour diameter (≥4 cm vs <4 cm), imaging characteristics (positive vs negative), and urine steroid metabolomics (low, medium, or high risk of ACC), separately and in combination, using a reference standard of histopathology and follow-up investigations. With respect to imaging characteristics, we also assessed the diagnostic utility of increasing the unenhanced CT tumour attenuation threshold from the recommended 10 Hounsfield units (HU) to 20 HU. FINDINGS Of 2169 participants recruited between Jan 17, 2011, and July 15, 2016, we included 2017 from 14 specialist centres in 11 countries in the final analysis. 98 (4·9%) had histopathologically or clinically and biochemically confirmed ACC. Tumours with diameters of 4 cm or larger were identified in 488 participants (24·2%), including 96 of the 98 with ACC (positive predictive value [PPV] 19·7%, 95% CI 16·2-23·5). For imaging characteristics, increasing the unenhanced CT tumour attenuation threshold to 20 HU from the recommended 10 HU increased specificity for ACC (80·0% [95% CI 77·9-82·0] vs 64·0% [61·4-66.4]) while maintaining sensitivity (99·0% [94·4-100·0] vs 100·0% [96·3-100·0]; PPV 19·7%, 16·3-23·5). A urine steroid metabolomics result indicating high risk of ACC had a PPV of 34·6% (95% CI 28·6-41·0). When the three tests were combined, in the order of tumour diameter, positive imaging characteristics, and urine steroid metabolomics, 106 (5·3%) participants had the result maximum tumour diameter of 4 cm or larger, positive imaging characteristics (with the 20 HU cutoff), and urine steroid metabolomics indicating high risk of ACC, for which the PPV was 76·4% (95% CI 67·2-84·1). 70 (3·5%) were classified as being at moderate risk of ACC and 1841 (91·3%) at low risk (negative predictive value 99·7%, 99·4-100·0). INTERPRETATION An unenhanced CT tumour attenuation cutoff of 20 HU should replace that of 10 HU for exclusion of ACC. A triple test strategy of tumour diameter, imaging characteristics, and urine steroid metabolomics improves detection of ACC, which could shorten time to surgery for patients with ACC and help to avoid unnecessary surgery in patients with benign tumours. FUNDING European Commission, UK Medical Research Council, Wellcome Trust, and UK National Institute for Health Research, US National Institutes of Health, the Claire Khan Trust Fund at University Hospitals Birmingham Charities, and the Mayo Clinic Foundation for Medical Education and Research.
-
3.
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.
-
4.
Metabolomics of Aerobic Exercise in Chronic Stroke Survivors: A Pilot Study.
Serra, MC, Accardi, CJ, Ma, C, Park, Y, Tran, V, Jones, DP, Hafer-Macko, CE, Ryan, AS
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association. 2019;(12):104453
-
-
Free full text
-
Abstract
BACKGROUND Understanding the metabolic response to exercise may aid in optimizing stroke management. Therefore, the purpose of this pilot study was to evaluate plasma metabolomic profiles in chronic stroke survivors following aerobic exercise training. METHODS Participants (age: 62 ± 1 years, body mass index: 31 ± 1 kg/m2, mean ± standard error of the mean) were randomized to 6 months of treadmill exercise (N = 17) or whole-body stretching (N = 8) with preintervention and postintervention measurement of aerobic capacity (VO2peak). Linear models for microarray data expression analysis was performed to determine metabolic changes over time, and Mummichog was used for pathway enrichment analysis following analysis of plasma samples by high-performance liquid chromatography coupled to ultrahigh resolution mass spectrometry. RESULTS VO2peak change was greater following exercise than stretching (18.9% versus -.2%; P < .01). Pathway enrichment analysis of differentially expressed metabolites results showed significant enrichment in 4 pathways following treadmill exercise, 3 of which (heparan-, chondroitin-, keratan-sulfate degradation) involved connective tissue metabolism and the fourth involve lipid signaling (linoleate metabolism). More pathways were altered in pre and post comparisons of stretching, including branched-chain amino acid, tryptophan, tyrosine, and urea cycle, which could indicate loss of lean body mass. CONCLUSIONS These preliminary data show different metabolic changes due to treadmill training and stretching in chronic stroke survivors and suggest that in addition to improved aerobic capacity, weight-bearing activity, like walking, could protect against loss of lean body mass. Future studies are needed to examine the relationship between changes in metabolomic profiles to reductions in cardiometabolic risk after treadmill rehabilitation.
-
5.
Multi-platform metabonomics unravel amino acids as markers of HIV/combination antiretroviral therapy-induced oxidative stress.
Sitole, LJ, Tugizimana, F, Meyer, D
Journal of pharmaceutical and biomedical analysis. 2019;:112796
Abstract
Infection by the human immunodeficiency virus (HIV) elicits an immune response wherein neutrophils produce reactive oxygen species (ROS) to defend against pathogen invasion. Consequently, disproportionate levels of ROS in relation to antioxidants lead to oxidative stress (OS), which plays a key role in HIV disease progression and pathogenesis. There is a close relationship between oxidative stress status and HIV infection/progression, both separately and in the presence of combination antiretroviral therapy (cART). Biomarkers of oxidative stress present an additional means of monitoring HIV disease progression and/or management. Thus, the objective of this study was to apply untargeted nuclear magnetic resonance (NMR)-based metabonomics followed by targeted quantitative gas chromatography-mass spectrometry (GC/MS) analyses to identify predictors of oxidative stress in HIV infected individuals, with or without cART. Untargeted NMR-based metabonomics allowed a global profiling of metabolic perturbations in HIV-infected sera. The cohort consisted of 21 HIV-negative control subjects (HIV-) and 113 HIV-infected individuals, of which 100 were on cART. Significant differences in metabolic features corresponding to changes in glucose, lipids, phenylalanine, glutamic acid, aspartic acid and branched amino acids were observed, which point to oxidative stress and insulin resistance. To further confirm oxidative stress, targeted GC/MS-based metabonomics, performed in succession, allowed for a quantitative description of a total of 9 oxidative stress-related metabolites. Significant up-regulation of aspartic acid, phenylalanine and glutamic acid were observed in the HIV-infected cohorts as compared to controls. Tryptophan and tyrosine were down-regulated whereas cystine levels were increased in HIV-infected and untreated individuals as compared to both HIV treated and negative control subjects. Pathway analysis also revealed 11 metabolic pathways to be significantly altered by infection and/or treatment. These pathways included aminoacyl-tRNA biosynthesis, nitrogen metabolism and phenylalanine, tyrosine and tryptophan biosynthesis. This pilot study demonstrated the use of multiplatform metabonomic strategies to elucidate metabolic markers that would be essential in predicting HIV/cART-induced oxidative stress. This could aid and contribute in HIV treatment and management programmes.
-
6.
Non-targeted metabolomic biomarkers and metabotypes of type 2 diabetes: A cross-sectional study of PREDIMED trial participants.
Urpi-Sarda, M, Almanza-Aguilera, E, Llorach, R, Vázquez-Fresno, R, Estruch, R, Corella, D, Sorli, JV, Carmona, F, Sanchez-Pla, A, Salas-Salvadó, J, et al
Diabetes & metabolism. 2019;(2):167-174
Abstract
AIM: To characterize the urinary metabolomic fingerprint and multi-metabolite signature associated with type 2 diabetes (T2D), and to classify the population into metabotypes related to T2D. METHODS A metabolomics analysis using the 1H-NMR-based, non-targeted metabolomic approach was conducted to determine the urinary metabolomic fingerprint of T2D compared with non-T2D participants in the PREDIMED trial. The discriminant metabolite fingerprint was subjected to logistic regression analysis and ROC analyses to establish and to assess the multi-metabolite signature of T2D prevalence, respectively. Metabotypes associated with T2D were identified using the k-means algorithm. RESULTS A total of 33 metabolites were significantly different (P<0.05) between T2D and non-T2D participants. The multi-metabolite signature of T2D comprised high levels of methylsuccinate, alanine, dimethylglycine and guanidoacetate, and reduced levels of glutamine, methylguanidine, 3-hydroxymandelate and hippurate, and had a 96.4% AUC, which was higher than the metabolites on their own and glucose. Amino-acid and carbohydrate metabolism were the main metabolic alterations in T2D, and various metabotypes were identified in the studied population. Among T2D participants, those with a metabotype of higher levels of phenylalanine, phenylacetylglutamine, p-cresol and acetoacetate had significantly higher levels of plasma glucose. CONCLUSION The multi-metabolite signature of T2D highlights the altered metabolic fingerprint associated mainly with amino-acid, carbohydrate and microbiota metabolism. Metabotypes identified in this patient population could be related to higher risk of long-term cardiovascular events and therefore require further studies. Metabolomics is a useful tool for elucidating the metabolic complexity and interindividual variation in T2D towards the development of stratified precision nutrition and medicine. Trial registration at www.controlled-trials.com: ISRCTN35739639.
-
7.
A pharmaco-metabolomics approach in a clinical trial of ALS: Identification of predictive markers of progression.
Blasco, H, Patin, F, Descat, A, Garçon, G, Corcia, P, Gelé, P, Lenglet, T, Bede, P, Meininger, V, Devos, D, et al
PloS one. 2018;(6):e0198116
Abstract
There is an urgent and unmet need for accurate biomarkers in Amyotrophic Lateral Sclerosis. A pharmaco-metabolomics study was conducted using plasma samples from the TRO19622 (olesoxime) trial to assess the link between early metabolomic profiles and clinical outcomes. Patients included in this trial were randomized into either Group O receiving olesoxime (n = 38) or Group P receiving placebo (n = 36). The metabolomic profile was assessed at time-point one (V1) and 12 months (V12) after the initiation of the treatment. High performance liquid chromatography coupled with tandem mass spectrometry was used to quantify 188 metabolites (Biocrates® commercial kit). Multivariate analysis based on machine learning approaches (i.e. Biosigner algorithm) was performed. Metabolomic profiles at V1 and V12 and changes in metabolomic profiles between V1 and V12 accurately discriminated between Groups O and P (p<5×10-6), and identified glycine, kynurenine and citrulline/arginine as the best predictors of group membership. Changes in metabolomic profiles were closely linked to clinical progression, and correlated with glutamine levels in Group P and amino acids, lipids and spermidine levels in Group O. Multivariate models accurately predicted disease progression and highlighted the discriminant role of sphingomyelins (SM C22:3, SM C24:1, SM OH C22:2, SM C16:1). To predict SVC from SM C24:1 in group O and SVC from SM OH C22:2 and SM C16:1 in group P+O, we noted a median sensitivity between 67% and 100%, a specificity between 66.7 and 71.4%, a positive predictive value between 66 and 75% and a negative predictive value between 70% and 100% in the test sets. This proof-of-concept study demonstrates that the metabolomics has a role in evaluating the biological effect of an investigational drug and may be a candidate biomarker as a secondary outcome measure in clinical trials.
-
8.
Investigating the early metabolic fingerprint of celiac disease - a prospective approach.
Kirchberg, FF, Werkstetter, KJ, Uhl, O, Auricchio, R, Castillejo, G, Korponay-Szabo, IR, Polanco, I, Ribes-Koninckx, C, Vriezinga, SL, Koletzko, B, et al
Journal of autoimmunity. 2016;:95-101
Abstract
OBJECTIVES AND STUDY In the development of Celiac Disease (CD) both genetic and environmental factors play a crucial role. The Human Leukocyte Antigen (HLA)-DQ2 and HLA-DQ8 loci are strongly related to the disease and are necessary but not sufficient for the development of CD. Therefore, increasing interest lies in examining the mechanisms of CD onset from the early beginning. Differences in serum and urine metabolic profiles between healthy individuals and CD patients have been reported previously. We aimed to investigate if the metabolic pathways were already altered in young, 4 month old infants, preceding the CD diagnosis. METHODS Serum samples were available for 230 four month old infants of the PreventCD project, a multicenter, randomized, double-blind, dietary intervention study. All children were positive for HLA-DQ2 and/or HLA-DQ8 and had at least one first-degree relative diagnosed with CD. Amino acids were quantified after derivatization with liquid chromatography - tandem mass spectrometry (MS/MS) and polar lipid concentrations (acylcarnitines, lysophosphatidylcholines, phosphatidylcholines, and sphingomyelins) were determined with direct infusion MS/MS. We investigated the association of the metabolic profile with (1) the development of CD up to the age of 8 years (yes/no), (2) with HLA-risk groups, (3) with the age at CD diagnosis, using linear mixed models and cox proportional hazards models. Gender, intervention group, and age at blood withdrawal were included as potential confounder. RESULTS By the end of 2014, thirty-three out of the 230 children (14%) were diagnosed with CD according to the ESPGHAN criteria. Median age at diagnosis was 3.4 years (IQR, 2.4-5.2). Testing each metabolite for a difference in the mean between healthy and CD children, we (1) could not identify a discriminant analyte or a pattern pointing towards an altered metabolism (Bonferroni corrected P > 0.05 for all). Metabolite concentrations (2) did not differ across the HLA-risk groups. When investigating the age at diagnosis using (3) survival models, we found no evidence for an association between the metabolic profile and the risk of a later CD diagnosis. CONCLUSION The metabolic profile at 4 months of age was not predictive for the development of CD up to the age of 8 years. Our results suggest that metabolic pathways reflected in serum are affected only later in life and that the HLA-genotype does not influence the serum metabolic profile in young infants before introduction of solid food.
-
9.
A distinct metabolic signature of human colorectal cancer with prognostic potential.
Qiu, Y, Cai, G, Zhou, B, Li, D, Zhao, A, Xie, G, Li, H, Cai, S, Xie, D, Huang, C, et al
Clinical cancer research : an official journal of the American Association for Cancer Research. 2014;(8):2136-46
-
-
Free full text
-
Abstract
PURPOSE Metabolic phenotyping has provided important biomarker findings, which, unfortunately, are rarely replicated across different sample sets due to the variations from different analytical and clinical protocols used in the studies. To date, very few metabolic hallmarks in a given cancer type have been confirmed and validated by use of a metabolomic approach and other clinical modalities. Here, we report a metabolomics study to identify potential metabolite biomarkers of colorectal cancer with potential theranostic value. EXPERIMENTAL DESIGN Gas chromatography-time-of-flight mass spectrometry (GC-TOFMS)-based metabolomics was used to analyze 376 surgical specimens, which were collected from four independent cohorts of patients with colorectal cancer at three hospitals located in China and City of Hope Comprehensive Cancer Center in the United States. Differential metabolites were identified and evaluated as potential prognostic markers. A targeted transcriptomic analysis of 29 colorectal cancer and 27 adjacent nontumor tissues was applied to analyze the gene expression levels for key enzymes associated with these shared metabolites. RESULTS A panel of 15 significantly altered metabolites was identified, which demonstrates the ability to predict the rate of recurrence and survival for patients after surgery and chemotherapy. The targeted transcriptomic analysis suggests that the differential expression of these metabolites is due to robust metabolic adaptations in cancer cells to increased oxidative stress as well as demand for energy, and macromolecular substrates for cell growth and proliferation. CONCLUSIONS These patients with colorectal cancer, despite their varied genetic background, mutations, pathologic stages, and geographic locations, shared a metabolic signature that is of great prognostic and therapeutic potential.
-
10.
[A metabonomic study on the treatment of diabetic nephropathy with traditional Chinese medicine tang-shen-fang].
Yu, H, Li, L, Liang, Q, Wang, Y, Li, P, Luo, G
Se pu = Chinese journal of chromatography. 2011;(4):320-4
Abstract
An ultra performance liquid chromatography-time of flight mass spectrometry (UP-LC/TOF MS)-based method for plasma metabolic fingerprinting analysis was established. Acquired data were analyzed by principal component analysis and orthogonal projection to latent structure-discriminant analysis. The effect of tang-shen-fang (TSF) on the treatment of diabetic nephropathy patients was evaluated. Significant changes were found after 3 and 6 months' treatment of TSF compared with placebo controls. Nineteen metabolites in plasma were identified as potential biomarkers, including lipids, fatty acids and amino acids. The present metabonomic study is helpful to grasp the changes of global metabolic networks during the treatment of TSF and to testify its clinical efficacy and understand its action mechanism.