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Metabolomics Analysis of Aspirin's Effects in Human Colon Tissue and Associations with Adenoma Risk.
Barry, EL, Fedirko, V, Uppal, K, Ma, C, Liu, K, Mott, LA, Peacock, JL, Passarelli, MN, Baron, JA, Jones, DP
Cancer prevention research (Philadelphia, Pa.). 2020;(10):863-876
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Abstract
Although substantial evidence supports aspirin's efficacy in colorectal cancer chemoprevention, key molecular mechanisms are uncertain. An untargeted metabolomics approach with high-resolution mass spectrometry was used to elucidate metabolic effects of aspirin treatment in human colon tissue. We measured 10,269 metabolic features in normal mucosal biopsies collected at colonoscopy after approximately 3 years of randomized treatment with placebo, 81 or 325 mg/day aspirin from 325 participants in the Aspirin/Folate Polyp Prevention Study. Linear regression was used to identify aspirin-associated metabolic features and network analysis was used to identify pathways and predict metabolite identities. Poisson regression was used to examine metabolic features associations with colorectal adenoma risk. We detected 471 aspirin-associated metabolic features. Aside from the carnitine shuttle, aspirin-associated metabolic pathways were largely distinct for 81 mg aspirin (e.g., pyrimidine metabolism) and 325 mg (e.g., arachidonic acid metabolism). Among aspirin-associated metabolic features, we discovered three that were associated with adenoma risk and could contribute to the chemopreventive effect of aspirin treatment, and which have also previously been associated with colorectal cancer: creatinine, glycerol 3-phosphate, and linoleate. The last two of these are in the glycerophospholipid metabolism pathway, which was associated with 81 mg aspirin treatment and provides precursors for the synthesis of eicosanoids from arachidonic acid upstream of cyclooxygenase inhibition by aspirin. Conversely, carnitine shuttle metabolites were increased with aspirin treatment and associated with increased adenoma risk. Thus, our untargeted metabolomics approach has identified novel metabolites and pathways that may underlie the effects of aspirin during early colorectal carcinogenesis.
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Impact of infant protein supply and other early life factors on plasma metabolome at 5.5 and 8 years of age: a randomized trial.
Kirchberg, FF, Hellmuth, C, Totzauer, M, Uhl, O, Closa-Monasterolo, R, Escribano, J, Gruszfeld, D, Gradowska, K, Verduci, E, Mariani, B, et al
International journal of obesity (2005). 2020;(1):69-81
Abstract
OBJECTIVES A high dairy protein intake in infancy, maternal pre-pregnancy BMI, and delivery mode are documented early programming factors that modulate the later risk of obesity and other health outcomes, but the mechanisms of action are not understood. METHODS The Childhood Obesity Project is a European multicenter, double-blind, randomized clinical trial that enrolled healthy infants. Participating infants were either breastfed (BF) or randomized to receive higher (HP) or lower protein (LP) content formula in the first year of life. At the ages 5.5 years (n = 276) and 8 years (n = 232), we determined plasma metabolites by liquid chromatography tandem-mass-spectrometry of which 226 and 185 passed quality control at 5.5 years and 8 years, respectively. We assessed the effects of infant feeding, maternal pre-pregnancy BMI, smoking in pregnancy, delivery mode, parity, birth weight and length, and weight gain (0-24 months) on the metabolome at 5.5 and 8 years. RESULTS At 5.5 years, plasma alpha-ketoglutarate and the acylcarnitine/BCAA ratios tended to be higher in the HP than in the LP group, but no metabolite reached statistical significance (Pbonferroni>0.09). There were no group differences at 8 years. Quantification of the impact of early programming factors revealed that the intervention group explained 0.6% of metabolome variance at both time points. Except for country of residence that explained 16% and 12% at 5.5 years and 8 years, respectively, none of the other factors explained considerably more variance than expected by chance. CONCLUSIONS Plasma metabolome was largely unaffected by feeding choice and other early programming factors and we could not prove the existence of a long term programming effect of the plasma metabolome.
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The Effect of Oligopin Supplementation on Hormonal and Metabolic Profiles in the Polycystic Ovary Syndrome: A Randomized Controlled Trial.
Qorbani, M, Sanginabadi, M, Mohajeri-Tehrani, MR, Karimi, S, Gerami, H, Mahdavi-Gorabi, A, Shirzad, N, Samadi, M, Baygi, F, Hosseini, S, et al
Frontiers in endocrinology. 2020;:590392
Abstract
BACKGROUND A double blind clinical trial was performed to evaluate whether the polycystic ovary syndrome (PCOS)-specific serum markers and metabolic parameters would change in the women with PCOS during the three-month administration of oligopin. METHODS In this double-blind multicenter trial, we randomly assigned 80 PCOS women, based on a 1:1 ratio, to receive oligopin (n= 40) or maltodextrin as placebo (n = 40) for up to 3 months. As PCOS-specific outcomes, we investigated the changes in testosterone, sex hormone binding globulin (SHBG), free androgen index (FAI), dehydroepiandrosterone (DHEA), follicle-stimulating hormone (FSH) and luteinizing hormone (LH). Secondary end points were metabolic (fasting glycaemia, hemoglobin A1c (HbA1c), lipids, insulin resistance (HOMA-IR)), anthropometrics parameters and blood pressure from the baseline to the end of treatment. We investigated serum transaminase, alkaline phosphatase (ALP), creatinine (Cr) and blood urea nitrogen (BUN) levels as hepatic and kidney outcomes, respectively. RESULTS The first participant was enrolled on April 18, 2018, and the last study visit took place on May 14, 2019. PCOS-specific serum parameters did not change during the three-month administration of oligopin (p > 0.05), except for a small increase in the FSH levels (p=0.03). Oligopin neither changed the metabolic profile nor the anthropometric parameters or blood pressure. ALP levels was significantly increased in placebo group, as compared with oligopin (p=0.01). CONCLUSION Oligopin supplementation does not seem to be exerting a beneficial effect on both hormonal and metabolic parameters in the women with PCOS. CLINICAL TRIAL REGISTRATION www.irct.ir, identifier IRCT20140406017139N3.
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Metabolome-wide association study of anti-epileptic drug treatment during pregnancy.
Walker, DI, Perry-Walker, K, Finnell, RH, Pennell, KD, Tran, V, May, RC, McElrath, TF, Meador, KJ, Pennell, PB, Jones, DP
Toxicology and applied pharmacology. 2019;:122-130
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Abstract
Pregnant women with epilepsy (PWWE) require continuous anti-epileptic drug (AED) treatment to avoid risk to themselves and fetal risks secondary to maternal seizures, resulting in prolonged AED exposure to the developing embryo and fetus. The objectives of this study were to determine whether high-resolution metabolomics is able to link the metabolite profile of PWWE receiving lamotrigine or levetiracetam for seizure control to associated pharmacodynamic (PD) biological responses. Untargeted metabolomic analysis of plasma obtained from 82 PWWE was completed using high-resolution mass spectrometry. Biological alterations due to lamotrigine or levetiracetam monotherapy were determined by a metabolome-wide association study that compared patients taking either drug to those who did not require AED treatment. Metabolic changes associated with AED use were then evaluated by testing for drug-dose associated metabolic variations and pathway enrichment. AED therapy resulted in drug-associated metabolic profiles recognizable within maternal plasma. Both the parent compounds and major metabolites were detected, and each AED was correlated with other metabolic features and pathways. Changes in metabolites and metabolic pathways important to maternal health and linked to fetal neurodevelopment were detected for both drugs, including changes in one‑carbon metabolism, neurotransmitter biosynthesis and steroid metabolism. In addition, decreased levels of 5-methyltetrahydrofolate and tetrahydrofolate were detected in women taking lamotrigine, which is consistent with recent findings showing increased risk of autism spectrum disorder traits in PWWE using AED. These results represent a first step in development of pharmacometabolomic framework with potential to detect adverse AED-related metabolic changes during pregnancy.
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The plasma metabolome of women in early pregnancy differs from that of non-pregnant women.
Handelman, SK, Romero, R, Tarca, AL, Pacora, P, Ingram, B, Maymon, E, Chaiworapongsa, T, Hassan, SS, Erez, O
PloS one. 2019;(11):e0224682
Abstract
BACKGROUND In comparison to the non-pregnant state, the first trimester of pregnancy is characterized by systemic adaptation of the mother. The extent to which these adaptive processes are reflected in the maternal blood metabolome is not well characterized. OBJECTIVE To determine the differences between the plasma metabolome of non-pregnant and pregnant women before 16 weeks gestation. STUDY DESIGN This study included plasma samples from 21 non-pregnant women and 50 women with a normal pregnancy (8-16 weeks of gestation). Combined measurements by ultrahigh performance liquid chromatography/tandem mass spectrometry and by gas chromatography/mass spectrometry generated molecular abundance measurements for each sample. Molecular species detected in at least 10 samples were included in the analysis. Differential abundance was inferred based on false discovery adjusted p-values (FDR) from Mann-Whitney-Wilcoxon U tests <0.1 and a minimum median abundance ratio (fold change) of 1.5. Alternatively, metabolic data were quantile normalized to remove sample-to-sample differences in the overall metabolite abundance (adjusted analysis). RESULTS Overall, 637 small molecules met the inclusion criteria and were tested for association with pregnancy; 44% (281/637) of small molecules had significantly different abundance, of which 81% (229/281) were less abundant in pregnant than in non-pregnant women. Eight percent (14/169) of the metabolites that remained significant in the adjusted analysis also changed as a function of gestational age. A pathway analysis revealed enrichment in steroid metabolites related to sex hormones, caffeine metabolites, lysolipids, dipeptides, and polypeptide bradykinin derivatives (all, FDR < 0.1). CONCLUSIONS This high-throughput mass spectrometry study identified: 1) differences between pregnant vs. non-pregnant women in the abundance of 44% of the profiled plasma metabolites, including known and novel molecules and pathways; and 2) specific metabolites that changed with gestational age.
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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.
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Metabolic profiles of second-generation antipsychotics in early psychosis: findings from the CAFE study.
Patel, JK, Buckley, PF, Woolson, S, Hamer, RM, McEvoy, JP, Perkins, DO, Lieberman, JA, ,
Schizophrenia research. 2009;(1-3):9-16
Abstract
OBJECTIVE To further define the metabolic profiles of second-generation antipsychotics during the treatment of young patients with early psychosis, with a view to better inform prescribing clinicians. METHOD Weight, body mass index (BMI), glucose, and serum lipids were measured in the 52-week Comparison of Atypicals for First Episode (CAFE) study, in which olanzapine, quetiapine, and risperidone were evaluated, and whose primary outcomes have been reported elsewhere. These metabolic data were analyzed using a mixed random coefficients model for continuous longitudinal measures and a logistic regression model for categorical responses. RESULTS Of the 400 patients recruited, 31% were overweight and 18% were obese at baseline, and 17 (4.3%) patients met criteria for metabolic syndrome. After 12 and 52 weeks of treatment, weight gain >or=7% from baseline was reported in 29.2% and 50.0% of quetiapine-treated patients, 59.8% and 80.0% of olanzapine-treated patients, and 32.5% and 57.6% of risperidone-treated patients, respectively. Weight gain after 12 and 52 weeks of treatment was estimated as [Least Squares Mean (SE)] 15.6 (+/-1.1) and 24.2 (+/-1.9) lb for olanzapine, 8.6 (+/-1.1) and 14.0 (+/-1.9) lb with risperidone and 7.9 (+/-1.1) and 12.1 (+/-1.8) lb for quetiapine respectively. In women, greater weight gain occurred during risperidone treatment compared with quetiapine treatment. By week 52, increases in BMI >or=1 unit occurred with significantly higher frequency in olanzapine-treated patients compared with quetiapine- or risperidone-treated patients. By 52 weeks, treatment-emergent metabolic syndrome was reported in 51 individuals (13.4% of the total population), of whom 22 were receiving olanzapine, 18 quetiapine, and 11 risperidone. Risperidone was associated with the smallest elevations in triglyceride and total cholesterol levels. CONCLUSION Weight gain and metabolic syndrome occur commonly even in young patients receiving antipsychotic treatment for early psychosis. Targeted interventions are therefore warranted from the onset of antipsychotic therapy.
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Superior predictive ability for death of a basic metabolic profile risk score.
May, HT, Horne, BD, Ronnow, BS, Renlund, DG, Muhlestein, JB, Lappé, DL, Pearson, RR, Carlquist, JF, Kfoury, AG, Bair, TL, et al
American heart journal. 2009;(5):946-54
Abstract
BACKGROUND The basic metabolic profile (BMP) is a common blood test containing information about standard blood electrolytes and metabolites. Although individual variables are checked for cardiovascular health and risk, combining them into a total BMP-derived score, as to maximize BMP predictive ability, has not been previously attempted. METHODS Patients (N = 279,337) that received a BMP and had long-term follow-up for death were studied. Risk models were created in a training group (60% of study population, n = 167,635), validated in a test group (40% of study population, n = 111,702), and confirmed in the NHANES III (Third National Health and Nutrition Examination Survey) participants (N = 17,752). The BMP models were developed for 30-day, 1-year, and 5-year death using logistic regression with adjustment for age and sex. The BMP parameters were categorized as low, normal, or high based on the standard range of normal. Glucose was categorized as normal, intermediate, and high. Creatinine >or=2 mg/dL was further categorized as very high. RESULTS Average age was 53.2 +/- 20.1 years, and 44.3% were male. The areas under the curve for the training and test groups for 30-day, 1-year, and 5-year death were 0.887 and 0.882, 0.850 and 0.848, and 0.858 and 0.847, respectively. The predictive ability of these risk scores was further confirmed in the NHANES III population and independent of the Framingham Risk Score. CONCLUSION In large, prospectively followed populations, a highly significant predictive ability for death was found for a BMP risk model. We propose a total BMP score as an optimization of this routine baseline test to provide an important new addition to risk prediction.