Plasma Metabolomic Profiling in 1391 Subjects with Overweight and Obesity from the SPHERE Study.
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SUBMITTER: Frigerio G
PROVIDER: S-EPMC8064361 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundIncidence rates of cardiovascular disease (CVD) are increasing, partly driven by the diabetes epidemic. Novel prediction tools and modifiable treatment targets are needed to enhance risk assessment and management. Plasma metabolite associations with subclinical atherosclerosis were investigated in the Diabetes Heart Study (DHS), a cohort enriched for type 2 diabetes (T2D).MethodsThe analysis included 700 DHS participants, 438 African Americans (AAs), and 262 European Americans (EAs), in whom coronary artery calcium (CAC) was assessed using ECG-gated computed tomography. Plasma metabolomics using liquid chromatography-mass spectrometry identified 853 known metabolites. An ancestry-specific marginal model incorporating generalized estimating equations examined associations between metabolites and CAC (log-transformed (CAC + 1) as outcome measure). Models were adjusted for age, sex, BMI, diabetes duration, date of plasma collection, time between plasma collection and CT exam, low-density lipoprotein cholesterol (LDL-C), and statin use.ResultsAt an FDR-corrected p-value < 0.05, 33 metabolites were associated with CAC in AAs and 36 in EAs. The androgenic steroids, fatty acid, phosphatidylcholine, and bile acid metabolism subpathways were associated with CAC in AAs, whereas fatty acid, lysoplasmalogen, and branched-chain amino acid (BCAA) subpathways were associated with CAC in EAs.ConclusionsStrikingly different metabolic signatures were associated with subclinical coronary atherosclerosis in AA and EA DHS participants.
| S-EPMC8653597 | biostudies-literature
Project description:IntroductionDuring the holy month of Ramadan, adult healthy Muslims are mandated to abstain from dawn to sunset, with free eating at night hours that may extend up to 12 h. The current work was designed to investigate the metabolomics changes incurred upon the observance of Ramadan diurnal intermittent fasting (RDIF).MethodsTwenty-five metabolically healthy participants with overweight and obesity (7 females and 18 males, with a mean age of 39.48 ± 10.0 years) were recruited for the study and were followed before and at the end of RDIF month. Dietary, anthropometric, biochemical, and physical activity assessments were performed before and at the end of the fasting month. The metabolomic assay was performed using liquid chromatography-mass spectrometry for the two-time points.Results and discussionMetabolomics assay revealed a significant reduction in a few metabolites. The analysis revealed that 27 metabolites differed significantly (P < 0.05) between pre-and post-RDIF. Among the differentially abundant metabolites, 23 showed a decrease with fasting, these included several amino acids such as aspartame, tryptophan, phenylalanine, histidine, and other metabolites including valeric acid, and cortisol. On the other hand, only four metabolites showed increased levels after RDIF including traumatic acid, 2-pyrrolidinone, PC[18:1(9Z)/18:1(9Z)], and L-sorbose. The MetaboAnalyst® platform reported that the top enriched metabolic pathways included: (1) histidine metabolism; (2) folate biosynthesis (3) phenylalanine, tyrosine, and tryptophan biosynthesis; (4) aminoacyltRNA biosynthesis; (5) caffeine metabolism; (6) vitamin B6 metabolism; and several other pathways relating to lipid metabolisms such as arachidonic acid metabolism, glycerophospholipid metabolism, and linoleic acid metabolism. In conclusion, RDIF entails significant changes in various metabolic pathways that reflect different dietary and lifestyle behaviors practiced during the fasting month.
| S-EPMC9868699 | biostudies-literature
Project description:BackgroundBiomarkers that allow detection of the onset of disease are of high interest since early detection would allow intervening with lifestyle and nutritional changes before the disease is manifested and pharmacological therapy is required. Our study aimed to improve the phenotypic characterization of overweight but apparently healthy subjects and to identify new candidate profiles for early biomarkers of obesity-related diseases such as cardiovascular disease and type 2 diabetes.Methodology/principal findingsIn a population of 56 healthy, middle-aged overweight subjects Body Mass Index (BMI), fasting concentration of 124 plasma proteins and insulin were determined. The plasma proteins are implicated in chronic diseases, inflammation, endothelial function and metabolic signaling. Random Forest was applied to select proteins associated with BMI and plasma insulin. Subsequently, the selected proteins were analyzed by clustering methods to identify protein clusters associated with BMI and plasma insulin. Similar analyses were performed for a second population of 20 healthy, overweight older subjects to verify associations found in population I. In both populations similar clusters of proteins associated with BMI or insulin were identified. Leptin and a number of pro-inflammatory proteins, previously identified as possible biomarkers for obesity-related disease, e.g. Complement 3, C Reactive Protein, Serum Amyloid P, Vascular Endothelial Growth Factor clustered together and were positively associated with BMI and insulin. IL-3 and IL-13 clustered together with Apolipoprotein A1 and were inversely associated with BMI and might be potential new biomarkers. CONCLUSION/ SIGNIFICANCE: We identified clusters of plasma proteins associated with BMI and insulin in healthy populations. These clusters included previously reported biomarkers for obesity-related disease and potential new biomarkers such as IL-3 and IL-13. These plasma protein clusters could have potential applications for improved phenotypic characterization of volunteers in nutritional intervention studies or as biomarkers in the early detection of obesity-linked disease development and progression.
| S-EPMC3009718 | biostudies-literature
Project description:ObjectiveThe purpose of this article is to characterize metabolomic profiles of four overweight/obesity (OWOB) and metabolic risk (MetRisk) phenotypes among 524 adolescents aged approximately 13 years.MethodsA four-level phenotype variable (non-OWOB and low MetRisk, non-OWOB and high MetRisk, OWOB and low MetRisk, and OWOB and high MetRisk) was created using BMI percentile to define OWOB, and high versus low MetRisk was derived as the fourth versus first to third quartiles of a z score calculated as the average of five externally standardized z scores for waist circumference, homeostatic model assessment of insulin resistance, high-density lipoprotein, triglycerides, and systolic blood pressure. Then associations of nine metabolite patterns derived from principal components analysis with phenotype after accounting for age, sex, race, and pubertal status were evaluated.ResultsFive metabolite patterns differed with respect to phenotype: factor 1 consisted of long-chain fatty acids and was lower among non-OWOB and high MetRisk (-0.90 [95% CI: -1.39 to -0.42]) versus non-OWOB and low MetRisk (referent); factors 5 (branched-chain amino acids), 8 (diacylglycerols), and 9 (steroid hormones) were highest among OWOB and high MetRisk; and factor 7 (long-chain acylcarnitines) was higher among non-OWOB and high MetRisk (0.47 [95% CI: 0.04 to 0.91]) and lower among OWOB and low MetRisk (-0.36 [95% CI: -0.68 to -0.04]).ConclusionsLong-chain fatty acids, branched-chain amino acids, acylcarnitines, diacylglycerols, and steroid hormones differed by weight status and metabolic phenotype.
| S-EPMC6980913 | biostudies-literature
Project description:BackgroundProliferative diabetic retinopathy (PDR), a sight-threatening retinopathy, is the leading cause of irreversible blindness in adults. Despite strict control of systemic risk factors, a fraction of patients with diabetes develop PDR, suggesting the existence of other potential pathogenic factors underlying PDR. This study aimed to investigate the plasma metabotype of patients with PDR and to identify novel metabolite markers for PDR. Biomarkers identified from this study will provide scientific insight and new strategies for the early diagnosis and intervention of diabetic retinopathy.MethodsA total of 1024 patients with type 2 diabetes were screened. To match clinical parameters between case and control subjects, patients with PDR (PDR, n = 21) or those with a duration of diabetes of ≥10 years but without diabetic retinopathy (NDR, n = 21) were assigned to the present case-control study. Distinct metabolite profiles of serum were examined using liquid chromatography-mass spectrometry (LC-MS).ResultsThe distinct metabolites between PDR and NDR groups were significantly enriched in 9 KEGG pathways (P < 0.05, impact > 0.1), namely, alanine, aspartate and glutamate metabolism, caffeine metabolism, beta-alanine metabolism, purine metabolism, cysteine and methionine metabolism, sulfur metabolism, sphingosine metabolism, and arginine and proline metabolism. A total of 63 altered metabolites played important roles in these pathways. Finally, 4 metabolites were selected as candidate biomarkers for PDR, namely, fumaric acid, uridine, acetic acid, and cytidine. The area under the curve for these biomarkers were 0.96, 0.95, 1.0, and 0.95, respectively.ConclusionsThis study suggested that impairment in the metabolism of pyrimidines, arginine and proline were identified as metabolic dysregulation associated with PDR. And fumaric acid, uridine, acetic acid, and cytidine might be potential biomarkers for PDR. Fumaric acid was firstly reported as a novel metabolite marker with no prior reports of association with diabetes or diabetic retinopathy, which might provide insights into potential new pathogenic pathways for diabetic retinopathy.
| S-EPMC6540396 | biostudies-literature
Project description:(R,S)-Ketamine produces rapid, robust, and sustained antidepressant effects in major depressive disorder. Specifically, its pharmacological efficacy in treatment refractory depression is considered a major breakthrough in the field. However, the mechanism of action of ketamine's rapid effect remains to be determined. In order to identify pathways that are responsible for ketamine's effect, a targeted metabolomic approach was carried out using a double-blind, placebo-controlled crossover design, with infusion order randomized with medication-free patients with treatment-resistant major depressive disorder (29 subjects) and healthy controls (25 subjects). The metabolomic profile of these subjects was characterized at multiple time points, and a comprehensive analysis was investigated between the following: MDD and healthy controls, treatment and placebo in both groups and the corresponding response to ketamine treatment. Ketamine treatment resulted in a general increase in circulating sphingomyelins, levels which were not correlated with response. Ketamine response resulted in more pronounced effects in the kynurenine pathway and the arginine pathway at 4 h post-infusion, where a larger decrease in circulating kynurenine levels and a larger increase in the bioavailability of arginine were observed in responders to ketamine treatment, suggesting possible mechanisms for response to ketamine treatment.
| S-EPMC6193489 | biostudies-literature
Project description:Depressive disorder is a multifactorial disease that is based on dysfunctions in mental and biological processes. The search for biomarkers can improve its diagnosis, personalize therapy, and lead to a deep understanding of the biochemical processes underlying depression. The purpose of this work was a metabolomic analysis of blood serum to classify patients with depressive disorders and healthy individuals using Compound Discoverer software. Using high-resolution mass spectrometry, blood plasma samples from 60 people were analyzed, of which 30 were included in a comparison group (healthy donors), and 30 were patients with a depressive episode (F32.11) and recurrent depressive disorder (F33.11). Differences between patient and control groups were identified using the built-in utilities in Compound Discoverer software. Compounds were identified by their accurate mass and fragment patterns using the mzCloud database and tentatively identified by their exact mass using the ChemSpider search engine and the KEGG, ChEBI, FDA UNII-NLM, Human Metabolome and LipidMAPS databases. We identified 18 metabolites that could divide patients with depressive disorders from healthy donors. Of these, only two compounds were tentatively identified using the mzCloud database (betaine and piperine) based on their fragmentation spectra. For three compounds ((4S,5S,8S,10R)-4,5,8-trihydroxy-10-methyl-3,4,5,8,9,10-hexahydro-2H-oxecin-2-one, (2E,4E)-N-(2-hydroxy-2-methylpropyl)-2,4-tetradecadienamide and 17α-methyl-androstan-3-hydroxyimine-17β-ol), matches were found in the mzCloud database but with low score, which could not serve as reliable evidence of their structure. Another 13 compounds were identified by their exact mass in the ChemSpider database, 9 (g-butyrobetaine, 6-diazonio-5-oxo-L-norleucine, 11-aminoundecanoic acid, methyl N-acetyl-2-diazonionorleucinate, glycyl-glycyl-argininal, dilaurylmethylamine, 12-ketodeoxycholic acid, dicetylamine, 1-linoleoyl-2-hydroxy-sn-glycero-3-PC) had only molecular formulas proposed, and 4 were unidentified. Thus, the use of Compound Discoverer software alone was not sufficient to identify all revealed metabolites. Nevertheless, the combination of the found metabolites made it possible to divide patients with depressive disorders from healthy donors.
| S-EPMC10890195 | biostudies-literature
Project description:ContextGlucagon is produced and released from the pancreatic alpha-cell to regulate glucose levels during periods of fasting. The main target for glucagon action is the liver, where it activates gluconeogenesis and glycogen breakdown; however, glucagon is postulated to have other roles within the body.ObjectiveWe sought to identify the circulating metabolites that would serve as markers of glucagon action in humans.MethodsIn this study (NCT03139305), we performed a continuous 72-hour glucagon infusion in healthy individuals with overweight/obesity. Participants were randomized to receive glucagon 12.5 ng/kg/min (GCG 12.5), glucagon 25 ng/kg/min (GCG 25), or a placebo control. A comprehensive metabolomics analysis was then performed from plasma isolated at several time points during the infusion to identify markers of glucagon activity.ResultsGlucagon (GCG 12.5 and GCG 25) resulted in significant changes in the plasma metabolome as soon as 4 hours following infusion. Pathways involved in amino acid metabolism were among the most affected. Rapid and sustained reduction of a broad panel of amino acids was observed. Additionally, time-dependent changes in free fatty acids and diacylglycerol and triglyceride species were observed.ConclusionThese results define a distinct signature of glucagon action that is broader than the known changes in glucose levels. In particular, the robust changes in amino acid levels may prove useful to monitor changes induced by glucagon in the context of additional glucagon-like peptide-1 or gastric inhibitory polypeptide treatment, as these agents also elicit changes in glucose levels.
| S-EPMC8317630 | biostudies-literature
Project description:BackgroundAlzheimer's disease (AD) is a complex neurological disorder with contributions from genetic and environmental factors. High-resolution metabolomics (HRM) has the potential to identify novel endogenous and environmental factors involved in AD. Previous metabolomics studies have identified circulating metabolites linked to AD, but lack of replication and inconsistent diagnostic algorithms have hindered the generalizability of these findings. Here we applied HRM to identify plasma metabolic and environmental factors associated with AD in two study samples, with cerebrospinal fluid (CSF) biomarkers of AD incorporated to achieve high diagnostic accuracy.MethodsLiquid chromatography-mass spectrometry (LC-MS)-based HRM was used to identify plasma and CSF metabolites associated with AD diagnosis and CSF AD biomarkers in two studies of prevalent AD (Study 1: 43 AD cases, 45 mild cognitive impairment [MCI] cases, 41 controls; Study 2: 50 AD cases, 18 controls). AD-associated metabolites were identified using a metabolome-wide association study (MWAS) framework.ResultsAn MWAS meta-analysis identified three non-medication AD-associated metabolites in plasma, including elevated levels of glutamine and an unknown halogenated compound and lower levels of piperine, a dietary alkaloid. The non-medication metabolites were correlated with CSF AD biomarkers, and glutamine and the unknown halogenated compound were also detected in CSF. Furthermore, in Study 1, the unknown compound and piperine were altered in MCI patients in the same direction as AD dementia.ConclusionsIn plasma, AD was reproducibly associated with elevated levels of glutamine and a halogen-containing compound and reduced levels of piperine. These findings provide further evidence that exposures and behavior may modify AD risks.
| S-EPMC6952314 | biostudies-literature
Project description:Intravenous busulfan doses are often personalized to a target plasma exposure (targeted busulfan) using an individual's busulfan clearance (BuCL). We evaluated whether BuCL could be predicted by a predose plasma panel of 841 endogenous metabolomic compounds (EMCs). In this prospective cohort of 132 hematopoietic cell transplantation (HCT) patients, all had samples collected immediately before busulfan administration (preBU) and 96 had samples collected 2 weeks before busulfan (2-week-preBU). BuCL was significantly associated with 37 EMCs after univariate linear regression analysis and controlling for false discovery (< 0.05) in the 132 preBU samples. In parallel, with preBU samples, we included all 841 EMCs in a least absolute shrinkage and selection operator-penalized regression which selected 13 EMCs as predominantly associated with BuCL. Then, we constructed a prediction model by estimating coefficients for these 13 EMCs, along with sex, using ordinary least-squares. When the resulting linear prediction model was applied to the 2-week-preBU samples, it explained 40% of the variation in BuCL (adjusted R2 = 0.40). Pathway enrichment analysis revealed 18 pathways associated with BuCL. Lysine degradation followed by steroid biosynthesis, which aligned with the univariate analysis, were the top two pathways. BuCL can be predicted before busulfan administration with a linear regression model of 13 EMCs. This pharmacometabolomics method should be prioritized over use of a busulfan test dose or pharmacogenomics to guide busulfan dosing. These results highlight the potential of pharmacometabolomics as a precision medicine tool to improve or replace pharmacokinetics to personalize busulfan doses.
| S-EPMC9888309 | biostudies-literature