Amy C. Harms

Roux-en-Y Gastric Bypass Surgery, but Not Calorie Restriction, Reduces Plasma Branched-Chain Amino Acids in Obese Women Independent of Weight Loss or the Presence of Type 2 Diabetes

OBJECTIVE Obesity and type 2 diabetes mellitus (T2DM) have been associated with increased levels of circulating branched-chain amino acids (BCAAs) that may be involved in the pathogenesis of insulin resistance. However, weight loss has not been consistently associated with the reduction of BCAA levels. RESEARCH DESIGN AND METHODS We included 30 obese normal glucose-tolerant (NGT) subjects, 32 obese subjects with T2DM, and 12 lean female subjects. Obese subjects underwent either a restrictive procedure (gastric banding [GB], a very low-calorie diet [VLCD]), or a restrictive/bypass procedure (Roux-en-Y gastric bypass [RYGB] surgery). Fasting blood samples were taken for the determination of amine group containing metabolites 4 weeks before, as well as 3 weeks and 3 months after the intervention. RESULTS BCAA levels were higher in T2DM subjects, but not in NGT subjects, compared with lean subjects. Principal component (PC) analysis revealed a concise PC consisting of all BCAAs, which showed a correlation with measures of insulin sensitivity and glucose tolerance. Only after the RYGB procedure, and at both 3 weeks and 3 months, were circulating BCAA levels reduced. CONCLUSIONS Our data confirm an association between deregulation of BCAA metabolism in plasma and insulin resistance and glucose intolerance. Three weeks after undergoing RYGB surgery, a significant decrease in BCAAs in both NGT as well as T2DM subjects was observed. After 3 months, despite inducing significant weight loss, neither GB nor VLCD induced a reduction in BCAA levels. Our results indicate that the bypass procedure of RYGB surgery, independent of weight loss or the presence of T2DM, reduces BCAA levels in obese subjects.

Pharmacometabolomics reveals that serotonin is implicated in aspirin response variability.

While aspirin is generally effective for prevention of cardiovascular disease, considerable variation in drug response exists, resulting in some individuals displaying high on‐treatment platelet reactivity. We used pharmacometabolomics to define pathways implicated in variation of response to treatment. We profiled serum samples from healthy subjects pre‐ and postaspirin (14 days, 81 mg/day) using mass spectrometry. We established a strong signature of aspirin exposure independent of response (15/34 metabolites changed). In our discovery (N = 80) and replication (N = 125) cohorts, higher serotonin levels pre‐ and postaspirin correlated with high, postaspirin, collagen‐induced platelet aggregation. In a third cohort, platelets from subjects with the highest levels of serotonin preaspirin retained higher reactivity after incubation with aspirin than platelets from subjects with the lowest serotonin levels preaspirin (72 ± 8 vs. 61 ± 11%, P = 0.02, N = 20). Finally, ex vivo, serotonin strongly increased platelet reactivity after platelet incubation with aspirin (+20%, P = 4.9 × 10−4, N = 12). These results suggest that serotonin is implicated in aspirin response variability.

Metabolomics Profiling for Identification of Novel Potential Markers in Early Prediction of Preeclampsia

Objective The first aim was to investigate specific signature patterns of metabolites that are significantly altered in first-trimester serum of women who subsequently developed preeclampsia (PE) compared to healthy pregnancies. The second aim of this study was to examine the predictive performance of the selected metabolites for both early onset [EO-PE] and late onset PE [LO-PE]. Methods This was a case-control study of maternal serum samples collected between 8+0 and 13+6 weeks of gestation from 167 women who subsequently developed EO-PE n = 68; LO-PE n = 99 and 500 controls with uncomplicated pregnancies. Metabolomics profiling analysis was performed using two methods. One has been optimized to target eicosanoids/oxylipins, which are known inflammation markers and the other targets compounds containing a primary or secondary biogenic amine group. Logistic regression analyses were performed to predict the development of PE using metabolites alone and in combination with first trimester mean arterial pressure (MAP) measurements. Results Two metabolites were significantly different between EO-PE and controls (taurine and asparagine) and one in case of LO-PE (glycylglycine). Taurine appeared the most discriminative biomarker and in combination with MAP predicted EO-PE with a detection rate (DR) of 55%, at a false-positive rate (FPR) of 10%. Conclusion Our findings suggest a potential role of taurine in both PE pathophysiology and first trimester screening for EO-PE.

Metabolic network failures in Alzheimer's disease—A biochemical road map

The Alzheimers Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimers disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance.

Integration of pharmacometabolomics with pharmacokinetics and pharmacodynamics: towards personalized drug therapy

Personalized medicine, in modern drug therapy, aims at a tailored drug treatment accounting for inter-individual variations in drug pharmacology to treat individuals effectively and safely. The inter-individual variability in drug response upon drug administration is caused by the interplay between drug pharmacology and the patients’ (patho)physiological status. Individual variations in (patho)physiological status may result from genetic polymorphisms, environmental factors (including current/past treatments), demographic characteristics, and disease related factors. Identification and quantification of predictors of inter-individual variability in drug pharmacology is necessary to achieve personalized medicine. Here, we highlight the potential of pharmacometabolomics in prospectively informing on the inter-individual differences in drug pharmacology, including both pharmacokinetic (PK) and pharmacodynamic (PD) processes, and thereby guiding drug selection and drug dosing. This review focusses on the pharmacometabolomics studies that have additional value on top of the conventional covariates in predicting drug PK. Additionally, employing pharmacometabolomics to predict drug PD is highlighted, and we suggest not only considering the endogenous metabolites as static variables but to include also drug dose and temporal changes in drug concentration in these studies. Although there are many endogenous metabolite biomarkers identified to predict PK and more often to predict PD, validation of these biomarkers in terms of specificity, sensitivity, reproducibility and clinical relevance is highly important. Furthermore, the application of these identified biomarkers in routine clinical practice deserves notable attention to truly personalize drug treatment in the near future.

Integrating metabolomics profiling measurements across multiple biobanks.

To optimize the quality of large scale mass-spectrometry based metabolomics data obtained from semiquantitative profiling measurements, it is important to use a strategy in which dedicated measurement designs are combined with a strict statistical quality control regime. This assures consistently high-quality results across measurements from individual studies, but semiquantitative data have been so far only comparable for samples measured within the same study. To enable comparability and integration of semiquantitative profiling data from different large scale studies over the time course of years, the measurement and quality control strategy has to be extended. We introduce a strategy to allow the integration of semiquantitative profiling data from different studies. We demonstrate that lipidomics data generated in samples from three different large biobanks acquired in the time course of 3 years can be effectively combined when using an appropriate measurement design and transfer model. This strategy paves the way toward an integrative usage of semiquantitative metabolomics data sets of multiple studies to validate biological findings in another study and/or to increase the statistical power for discovery of biomarkers or pathways by combining studies.

Metabolic characterization of the natural progression of chronic hepatitis B

BackgroundWorldwide, over 350 million people are chronically infected with the hepatitis B virus (HBV) and are at increased risk of developing progressive liver diseases. The confinement of HBV replication to the liver, which also acts as the central hub for metabolic and nutritional regulation, emphasizes the interlinked nature of host metabolism and the disease. Still, the metabolic processes operational during the distinct clinical phases of a chronic HBV infection—immune tolerant, immune active, inactive carrier, and HBeAg-negative hepatitis phases—remains unexplored.MethodsTo investigate this, we conducted a targeted metabolomics approach on serum to determine the metabolic progression over the clinical phases of chronic HBV infection, using patient samples grouped based on their HBV DNA, alanine aminotransferase, and HBeAg serum levels.ResultsOur data illustrate the strength of metabolomics to provide insight into the metabolic dysregulation experienced during chronic HBV. The immune tolerant phase is characterized by the speculated viral hijacking of the glycerol-3-phosphate–NADH shuttle, explaining the reduced glycerophospholipid and increased plasmalogen species, indicating a strong link to HBV replication. The persisting impairment of the choline glycerophospholipids, even during the inactive carrier phase with minimal HBV activity, alludes to possible metabolic imprinting effects. The progression of chronic HBV is associated with increased concentrations of very long chain triglycerides together with citrulline and ornithine, reflective of a dysregulated urea cycle peaking in the HBV envelope antigen-negative phase.ConclusionsThe work presented here will aid in future studies to (i) validate and understand the implication of these metabolic changes using a thorough systems biology approach, (ii) monitor and predict disease severity, as well as (iii) determine the therapeutic value of the glycerol-3-phosphate–NADH shuttle.

Metabolomics profiling of the free and total oxidised lipids in urine by LC-MS/MS: application in patients with rheumatoid arthritis

Oxidised lipids, covering enzymatic and auto-oxidation-synthesised mediators, are important signalling metabolites in inflammation while also providing a readout for oxidative stress, both of which are prominent physiological processes in a plethora of diseases. Excretion of these metabolites via urine is enhanced through the phase-II conjugation with glucuronic acid, resulting in increased hydrophilicity of these lipid mediators. Here, we developed a bovine liver-β-glucuronidase hydrolysing sample preparation method, using liquid chromatography coupled to tandem mass spectrometry to analyse the total urinary oxidised lipid profile including the prostaglandins, isoprostanes, dihydroxy-fatty acids, hydroxy-fatty acids and the nitro-fatty acids. Our method detected more than 70 oxidised lipids biosynthesised from two non-enzymatic and three enzymatic pathways in urine samples. The total oxidised lipid profiling method was developed and validated for human urine and was demonstrated for urine samples from patients with rheumatoid arthritis. Pro-inflammatory mediators PGF2α and PGF3α and oxidative stress markers iPF2α- IV, 11-HETE and 14-HDoHE were positively associated with improvement of disease activity score. Furthermore, the anti-inflammatory nitro-fatty acids were negatively associated with baseline disease activity. In conclusion, the developed methodology expands the current metabolic profiling of oxidised lipids in urine, and its application will enhance our understanding of the role these bioactive metabolites play in health and disease.

Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-α Inhibitors in Rheumatoid Arthritis

In clinical practice, approximately one-third of patients with rheumatoid arthritis (RA) respond insufficiently to TNF-α inhibitors (TNFis). The aim of the study was to explore the use of a metabolomics to identify predictors for the outcome of TNFi therapy, and study the metabolomic fingerprint in active RA irrespective of patients’ response. In the metabolomic profiling, lipids, oxylipins, and amines were measured in serum samples of RA patients from the observational BiOCURA cohort, before start of biological treatment. Multivariable logistic regression models were established to identify predictors for good- and non-response in patients receiving TNFi (n = 124). The added value of metabolites over prediction using clinical parameters only was determined by comparing the area under receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive- and negative predictive value and by the net reclassification index (NRI). The models were further validated by 10-fold cross validation and tested on the complete TNFi treatment cohort including moderate responders. Additionally, metabolites were identified that cross-sectionally associated with the RA disease activity score based on a 28-joint count (DAS28), erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP). Out of 139 metabolites, the best-performing predictors were sn1-LPC(18:3-ω3/ω6), sn1-LPC(15:0), ethanolamine, and lysine. The model that combined the selected metabolites with clinical parameters showed a significant larger AUC-ROC than that of the model containing only clinical parameters (p = 0.01). The combined model was able to discriminate good- and non-responders with good accuracy and to reclassify non-responders with an improvement of 30% (total NRI = 0.23) and showed a prediction error of 0.27. For the complete TNFi cohort, the NRI was 0.22. In addition, 88 metabolites were associated with DAS28, ESR or CRP (p<0.05). Our study established an accurate prediction model for response to TNFi therapy, containing metabolites and clinical parameters. Associations between metabolites and disease activity may help elucidate additional pathologic mechanisms behind RA.

First-Trimester Serum Acylcarnitine Levels to Predict Preeclampsia: A Metabolomics Approach.

Objective. To expand the search for preeclampsia (PE) metabolomics biomarkers through the analysis of acylcarnitines in first-trimester maternal serum. Methods. This was a nested case-control study using serum from pregnant women, drawn between 8 and 14 weeks of gestational age. Metabolites were measured using an UPLC-MS/MS based method. Concentrations were compared between controls (n = 500) and early-onset- (EO-) PE (n = 68) or late-onset- (LO-) PE (n = 99) women. Metabolites with a false discovery rate <10% for both EO-PE and LO-PE were selected and added to prediction models based on maternal characteristics (MC), mean arterial pressure (MAP), and previously established biomarkers (PAPPA, PLGF, and taurine). Results. Twelve metabolites were significantly different between EO-PE women and controls, with effect levels between −18% and 29%. For LO-PE, 11 metabolites were significantly different with effect sizes between −8% and 24%. Nine metabolites were significantly different for both comparisons. The best prediction model for EO-PE consisted of MC, MAP, PAPPA, PLGF, taurine, and stearoylcarnitine (AUC = 0.784). The best prediction model for LO-PE consisted of MC, MAP, PAPPA, PLGF, and stearoylcarnitine (AUC = 0.700). Conclusion. This study identified stearoylcarnitine as a novel metabolomics biomarker for EO-PE and LO-PE. Nevertheless, metabolomics-based assays for predicting PE are not yet suitable for clinical implementation.