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Metabolic Profile

Metabolic Profile: A comprehensive assessment of an individual's metabolic state, encompassing the analysis of various biochemical markers and physiological parameters.
This holistic evaluation provides insights into the overall metabolic functioning, including the efficiency of energy production, nutrient utilization, and waste management.
The Metabolic Profile can help identify potential imbalances, metabolic disorders, or risk factors for chronic conditions, enabling personalized interventions to optimize metabolic health and well-being.
By understanding one's Metabolic Profile, individuals can make informed decisions about diet, exercise, and lifestyle adjustments to enhance metabolc perfromance and reduce the risk of metabolic-related diseases.

Most cited protocols related to «Metabolic Profile»

1
Gut Microbiome Taxonomic and Functional Analysis
Cited 192 times
Human gut 16S rRNA sequences were prepared as described in Eckburg et al. and Ley et al. (2006) and are available in GenBank, accession numbers: DQ793220-DQ802819, DQ803048, DQ803139-DQ810181, DQ823640-DQ825343, AY974810-AY986384. In our experiments we assigned all 16S sequences to taxa using a naïve Bayesian classifier currently employed by the Ribosomal Database Project II (RDP) [30] (link). COG profiles of 13 human gut microbiomes were obtained from the supplementary material of Kurokawa et al.[34] (link). We acquired metabolic functional profiles of 85 metagenomes from the online supplementary materials of Dinsdale et al. (2008) (http://www.theseed.org/DinsdaleSupplementalMaterial/).
White J.R., Nagarajan N, & Pop M. (2009). Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples. PLoS Computational Biology, 5(4), e1000352.
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Publication 2009
Gastrointestinal Microbiome Homo sapiens Human Microbiome Metabolic Profile Metagenome Ribosomes RNA, Ribosomal, 16S
2
Analyzing Gut Microbiome Diversity
Cited 119 times
Fecal samples were collected from each individual. Community DNA was prepared and used for pyrosequencing (454 Life Sciences), as well as for PCR and sequencing of bacterial 16S rRNA genes. Shotgun reads were mapped to reference genomes using the NCBI ‘non-redundant’ database, KEGG17 (link), STRING18 (link), CAZy (http://www.cazy.org/), and a 44-member human gut microbial genomes database. Metabolic reconstructions were performed based on CAZy, KEGG, and STRING annotations. The relative abundance of KEGG metabolic pathways is referred to as a ‘metabolic profile.’
Turnbaugh P.J., Hamady M., Yatsunenko T., Cantarel B.L., Duncan A., Ley R.E., Sogin M.L., Jones W.J., Roe B.A., Affourtit J.P., Egholm M., Henrissat B., Heath A.C., Knight R, & Gordon J.I. (2008). A core gut microbiome in obese and lean twins. Nature, 457(7228), 480-484.
Publication 2008
Feces Genes, Bacterial Genome Genome, Microbial Homo sapiens Metabolic Profile Reconstructive Surgical Procedures RNA, Ribosomal, 16S
3
Metabolomics data imputation evaluation
Cited 70 times
A total number of four real-world metabolomics data sets were included in this study. The first two were applied to evaluate the performance of different imputation methods. Since the measurements required comparisons between imputed data and original data, a complete raw data set was needed in our studies. Thus, we removed all missing values in our original data beforehand and left a complete data set for consequential analysis.

This data set includes a total of 977 de-identified subjects and 75 metabolites without missing values. These metabolites include free fatty acids, amino acids, and bile acids, which were identified using both GC/MS-based non-targeted analysis and LC/MS-based targeted metabolomics approach. It served as a large sample size data set for label-free evaluation.

This data set was collected from a study of comparing metabolic profiles between obese subjects with diabetes mellitus and healthy controls28 (link),29 (link). After filtering all missing values, this data set contained a total number of 198 subjects (70 patients, 128 healthy controls) and 130 metabolites. These metabolites include free fatty acids, amino acids, and bile acids that were identified using LC/MS-based targeted metabolomics approaches. It served as medium sample size data set for both label-free and labeled data evaluation.

Then the other two datasets with missing elements were applied to determine the types of missing values present in different metabolomics datasets.

The is a GC/MS profiling data that contains 37 samples and 110 metabolites identified, with 317 missing values and 221 of them were re-identified manually.

This is a targeted LC/MS metabolomics dataset, which includes 40 samples and 41 metabolites, with 88 missing elements and 26 of them were re-identified manually.

Wei R., Wang J., Su M., Jia E., Chen S., Chen T, & Ni Y. (2018). Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data. Scientific Reports, 8, 663.
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Publication 2018
Amino Acids Bile Acids Diabetes Mellitus Gas Chromatography-Mass Spectrometry Metabolic Profile Nonesterified Fatty Acids Obesity Patients
4
Computational Metabolomics: Correlations and Identification
Cited 43 times
An important advantage of computational metabolomics lies in the use of correlations among ion signals to aid in determination of chemical identity. Metabolites are interconnected by a series of biochemical reactions, and this network of metabolites is organized in a hierarchical manner such that many small modules combine to form larger modules.56 (link),57 Correlation-based network and modularity analysis is one approach to elucidate the association structure of metabolites. Although there are several mechanisms that could lead to correlations between metabolites, the association structure can be used to identify ions derived from the same metabolite,58 (link)–60 (link) identify biotransformations,61 (link) and detect associations between environmental exposures and endogenous metabolites.15 (link)For high abundance unidentified chemicals, multiple spectral features arising from a single chemical provide valuable structural information to characterize a chemical. A network of ions where a pair of ions is linked if their correlation exceeds the significance threshold, e.g., |r| > 0.8, can be generated to identify isotopes, adducts, and in-source fragments associated with a chemical (Figure 4). A similar approach can be used to identify biotransformations and other related metabolites.60 (link) Metabolome-wide association studies (MWAS) allow identification of associations between a specific target variable, e.g., cotinine levels in individuals, and metabolic profiles.8 (link),62 (link)–64 (link) In an MWAS, statistical tests are performed for association of a parameter (e.g., disease biomarker, chemical, or other measured parameter) with each m/z feature to test for significance of association. Application of targeted MWAS using correlation-based criteria identified choline-related metabolites and demonstrated similarity between correlation patterns of choline in different species (Figure 5).64 (link)Correlation-based network analysis can also facilitate identification of in-source fragments. Gas-chromatography–mass spectrometry with electron ionization sources results in a large number of characteristic spectra indicative of chemical functional groups and structure.61 (link),65 Electrospray ionization can produce in-source fragmentation (e.g., loss of NH3, H2O, CHOOH, etc.) from electrical potentials or heat applied in the ion source.66 (link),67 (link) Because in-source fragments can mimic accurate masses of other common metabolites, computational methods that identify adducts, isotopes, and in-source fragments (based on clustering of highly correlated coeluting ions) increases the ability to correctly assign chemical identities. An example is the in-source formation of pyroglutamate from glutamine or glutamate.68 (link) The identification of in-source fragments requires consideration of chromatographic conditions to separate possible coeluting chemicals, as well as ion source conditions. When using soft ionization techniques, in-source fragmentation is only commonly observed for highly abundant metabolites, many low abundance chemicals will generate only a single detectable signal.3 (link),18 (link) To ensure detected, unannotated ions are unique chemicals, it is important to perform targeted MWAS to exclude the possibility of a signal originating from source fragments, adducts, and/or isotopes. To increase confidence of chemical identification, alternative detection methods with increased sensitivity for unknown chemicals and methods for defining unknown ions will be needed.
In addition to characterizing ions arising from known chemicals, MWAS using univariate and multivariate approaches can be used to generate hypotheses about biochemical roles of features with no database matches. This process uses targeted MWAS with validated metabolites or xMWAS, where “x” corresponds to other–omes (transcriptome, microbiome, genome, etc.). Krumsiek et al. used a systems-level approach where they combined genome-wide association analysis, knowledge-based pathway information, and metabolic networks to predict the identity of unknown metabolites.69 (link) Other studies have used integrative methods based on partial least-squares regression (PLS) to determine correlations between the metabolome and the transcriptome,70 proteome,71 (link) and microbiome.72 (link) These methods combined with pathway and literature based information can provide alternative approaches for generating hypotheses about chemical identity, particularly for low abundance chemicals.
Uppal K., Walker D.I., Liu K., Li S., Go Y.M, & Jones D.P. (2016). Computational Metabolomics: A Framework for the Million Metabolome. Chemical research in toxicology, 29(12), 1956-1975.
Publication 2016
Biological Markers Biotransformation Choline Chromatography Cotinine Electricity Electrons Environmental Exposure Gas Chromatography-Mass Spectrometry Genome Genome-Wide Association Study Glutamate Glutamine Hypersensitivity Ions Isotopes Metabolic Networks Metabolic Profile Metabolome Microbiome Proteome Pyroglutamate Transcriptome
5
Metabolite Extraction from Cultured Cells
Cited 22 times

Living cells, cultured on petri dishes or multi-well plates. Cells can be incubated with a labeled tracer (for example: 13C or 15N) for downstream flux analysis

Cells should be confluent in the wells, with a consistent cell number between samples. The amount of cells will vary depending on cell type used, but we have found using around 1 million cells in each well of a 6-well multiwell plate gives good results for both techniques.

0.9% NaCl at room temperature

High purity (MS grade) methanol at −20 °C

High purity (MS grade) chloroform at −20 °C

Millipore or equivalently pure water on ice

Cell scrapers

Eppendorf tube shaker at 4 °C

Centrifuge at 4 °C

Note: This list does not include generic laboratory equipment, which are assumed to be available.

The metabolic profile of a cell can change in as little as a few seconds. Therefore, the most important step in metabolite extraction is the quenching of metabolism; this ensures that the metabolic pathways in the cells do not continue to function, and that the cellular state at the point of extraction is as close as possible to the desired analysis time point [1] (link). This quenching must be performed quickly. There has been much discussion as to which extraction fluids are best for quenching and measuring metabolites [2,3] ; however, it is generally agreed that a mixture of water and methanol provides the best extraction efficiency with minimal loss. Both fluids are added directly to the cells, and should be kept as cold as possible (methanol at −20 °C and water on ice).
Once the metabolic processes have been quenched, the next step is to lyse the cells, separating both the polar and non-polar metabolites from the other cellular substances at the same time. While methanol and water will extract the polar metabolites from a sample, non-polar metabolites must be separated with a non-polar solvent. Therefore, we use chloroform [4] (link) with the methanol/water mixture to separate the polar and non-polar metabolites efficiently. Adherent cells quenched with methanol and water are scraped from the multi-well plates and added to cold chloroform to allow for separation of polar and non-polar phases. These extracts are agitated to complete cell lysis and centrifuged to fully separate the layers.
This is a crucial step for experimental consistency; different amounts of cells in different samples will lead to incorrect comparisons of metabolite levels (which can also occur with cell seeding). Therefore, care should be taken to adequately scrape all wells and transfer as much cellular material as possible from the wells to the chloroform.
After these steps, the cells are shaken to completely lyse the membranes allowing for a more efficient extraction of all possible biomolecules. After shaking, there should be a clear separation between the polar and non-polar phase for the metabolites, with a well-defined interphase containing proteins and nucleic acids.
Sapcariu S.C., Kanashova T., Weindl D., Ghelfi J., Dittmar G, & Hiller K. (2014). Simultaneous extraction of proteins and metabolites from cells in culture. MethodsX, 1, 74-80.
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Publication 2014
Cells Chloroform Cold Temperature Fever Generic Drugs Hyperostosis, Diffuse Idiopathic Skeletal Ice Interphase Metabolic Profile Metabolism Methanol Neoplasm Metastasis Normal Saline Nucleic Acids Proteins Solvents Somatostatin-Secreting Cells Tissue, Membrane

Most recents protocols related to «Metabolic Profile»

1
Dairy Cow Metabolism and Milk Yield
Samples of feedstuffs were collected daily (day −2 to 8) and lyophilized in a Genesis Freeze Dryer 25 (Hucoa Erlöss, SA/Thermo Fisher Scientific) to determine their chemical composition and FA profile. Hay was offered daily at 08:00 h as a single meal in individual troughs, where cows were tied up until they finished their ration, during approximately 2 h. ALPRO feeding stations were programmed to offer 3 kg of concentrate daily (as-fed basis) to all the cows during the basal and refeeding periods. Individual concentrate intake was recorded daily.
The BCS was recorded upon calving, 30 DIM, and on experimental period day −2 and 8. It was determined by a trained person on a 1–5 scale, based on estimating the fat covering ribs, loin, and tailhead (Lowman et al., 1976 ). Cows were weighed on an electronic scale upon calving and then at 07:00 h on 30 and 31 DIM and on experiment day −2, 1, 3, 5, 6, and 8. Milk yield was estimated on the same days by the weight-suckle-weight technique (Le Neindre and Dubroeucq, 1973 (link)). Calves were weighed before and after the two daily 30-min periods in which they had access to suckle their dams. The daily milk yield was estimated as the sum of the milk consumed by the calf in these two suckling periods. Milk samples were manually taken from each dam after the morning suckling. Five minutes before the manual extraction, all cows received an intramuscular injection of oxytocin (40 UI, Facilpart, Laboratorios Syva, León, Spain) to accelerate the letdown of the residual milk. A 100-mL sample was collected to determine milk composition, added with sodium azide (PanReac) as a preservative and refrigerated at 4 °C until the analysis. To determine FA composition, a second 40-mL sample was collected, lyophilized, and stored at −20 °C until analyzed.
Cows were bled on the same experiment days described above to assess their metabolic profile. Blood samples were collected from the coccygeal vein at 07:00 h after suckling and before offering hay. Heparinized tubes (BD Vacutainer Becton-Dickenson and Company) were used for the β-hydroxybutyrate (BHB) and MDA determinations, and the tubes that contained K2 EDTA (BD Vacutainer Becton-Dickenson and Company) were used to analyze glucose, NEFA, and urea concentrations. Immediately after collection, blood samples were centrifuged at 3,500 rpm for 20 min at 4 °C. Plasma was collected and frozen at −20 °C until further analyses.
Orquera-Arguero K.G., Blanco M., Bertolín J.R., Ferrer J, & Casasús I. (2023). Performance and milk fatty acid profile of beef cows with a different energy status with short nutrient restriction and refeeding. Journal of Animal Science, 101, skad053.
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Publication 2023
BLOOD Cattle chemical composition Coccyx Edetic Acid Freezing Glucose Hydroxybutyrates Intramuscular Injection Metabolic Profile Milk, Cow's Milk Ejection Nonesterified Fatty Acids Oxytocin Pharmaceutical Preservatives Plasma Ribs Scheuermann's Disease Sodium Azide Urea Veins
2
Calculating Dairy Cow Energy Balance and Milk Fatty Acids
The chemical composition of feedstuffs was employed to calculate their NE content using INRA equations (INRA, 2007 ). Individual EB was estimated by calculating the difference between inputs (NE intake) and outputs (NE for maintenance and NE for lactation) (INRA, 2007 ). Net energy intake was estimated from the individual intake and energy contents of feedstuffs. Net energy for maintenance was calculated from the individual metabolic weight. Net energy for production was obtained using the milk yield, fat, and protein contents in milk.
In milk, FA were grouped according to their degree of saturation as saturated fatty acid (SFA), monounsaturated fatty acid (MUFA), and polyunsaturated fatty acid (PUFA) ­according to their origin from de novo synthesis (C4:0–C15:1), of mixed origin (C16:0–C16:1), and from mobilization (≥C17:0) (Palmquist, 2009 ). The C18:1 cis-9 to C15:0 FA ratio was calculated to assess its relation with the EB and metabolic profile.
Orquera-Arguero K.G., Blanco M., Bertolín J.R., Ferrer J, & Casasús I. (2023). Performance and milk fatty acid profile of beef cows with a different energy status with short nutrient restriction and refeeding. Journal of Animal Science, 101, skad053.
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Publication 2023
Anabolism chemical composition CISH protein, human Fatty Acids, Monounsaturated Lactation Metabolic Profile Milk Milk Proteins Polyunsaturated Fatty Acids Saturated Fatty Acid
3
Metabolic Profiling of Muscle and Serum
The 0.50–9.50 ppm region of the proton spectra of mice muscle (gastrocnemius) was automatically segmented into integrated regions (buckets) of 0.02 ppm each using the AMIX 3.6 package (Bruker Biospin, Germany). The 4.55–5.15 ppm region around the water resonance was excluded, and the binned regions were normalized to the total spectrum area. For sera, we used the selected 0.60–8.60 ppm spectral area, and spectra were analyzed as above, excluding the 4.46–5.16 ppm water resonance region. Multivariate statistical data analysis was applied to the muscle and serum dataset to differentiate treated/untreated mdx and healthy profiles according to their metabolic content. Each dataset was reshaped as a matrix and imported into SIMCA 14 package (Umetrics, Umea, Sweden), where unsupervised PCA followed by supervised OPLS‐DA discriminant analyses was performed (Eriksson et al, 2006 ). PCA was first applied to check outliers and uncover trends and clusters, while OPLS‐DA was used to improve group discrimination. Moreover, O2PLS analysis was performed to generate a bilinear and joint model for NMR and gut microbiota data. We also generated correlation maps with hierarchical clustering by combining microbiota families values and selected polar metabolite buckets considering Euclidean distance for the metrics and the WARD method for clustering criterion. The performance of each multivariate model was evaluated via R2 (the goodness of fit) and Q2 (the goodness of prediction) parameters. Each model was validated by a 7‐round internal iterative cross‐validation routine, permutation test response (800 repeats), and analysis of variance (ANOVA testing of cross‐validated predictive residuals). Selected and isolated signals with ¦Pcorr¦ ≥ 0.7, VIP (variable importance in the projection) > 1 were then considered for univariate statistical analysis and ANOVA test with Bonferroni correction.
Kalkan H., Pagano E., Paris D., Panza E., Cuozzo M., Moriello C., Piscitelli F., Abolghasemi A., Gazzerro E., Silvestri C., Capasso R., Motta A., Russo R., Di Marzo V, & Iannotti F.A. (2023). Targeting gut dysbiosis against inflammation and impaired autophagy in Duchenne muscular dystrophy. EMBO Molecular Medicine, 15(3), e16225.
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Publication 2023
Discrimination, Psychology Gastrointestinal Microbiome Joints Metabolic Profile Mice, House Microbial Community Microtubule-Associated Proteins Muscle, Gastrocnemius Muscle Tissue neuro-oncological ventral antigen 2, human Protons Serum Vibration
4
Metabolic Flux Profiling of Single Cells
Single-cell flux estimation analysis (scFEA; Alghamdi et al., 2021 (link)) was used to identify the single-cell metabolic flux profiles. A total of 168 metabolic modules were directly downloaded from the algorithm’s official GitHub page.4 Using default parameters, the FindMarkers function (the nonparametric Wilcoxon rank sum test) was used for differential expression analysis. Differential expression genes with p_val_adj < 0.05 were then selected for futher analysis. Using default parameters, the Monocle2 (version 2.22.0; Trapnell et al., 2014 (link)) algorithm was used to construct a single-cell pseudotime trajectory. We used the 2,000 most highly variable genes for analysis and identified 5 distinct cell states. State-specific genes were identified using differentialGeneTest, which compares the expression of genes between each state and the remaining four states. The trajectories were constructed using DDRTree.
Fan L.Y., Yang J., Li M.L., Liu R.Y., Kong Y., Duan S.Y., Guo G.Y., Yang J.H, & Xu Y.M. (2023). Single-nucleus transcriptional profiling uncovers the reprogrammed metabolism of astrocytes in Alzheimer’s disease. Frontiers in Molecular Neuroscience, 16, 1136398.
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Publication 2023
Cells Gene Expression Genes Metabolic Profile Single-Cell Analysis
5
Metabolic Profiling for Group Discrimination
Clinical data were analysed using SPSS Statistics 25 software (IBM®, Armonk, NY, USA). Data are represented as the mean ± standard deviation or median and interquartile range. Continuous variables were compared using Student’s t test or the Mann−Whitney U test. Student’s t test is used when two samples are small and meet the conditions of normal distribution and homogeneity of variance. The Mann−Whitney U test was used when the samples did not meet the conditions of normal distribution and homogeneity of variance. Categorical variables between the two groups were compared by Fisher’s exact probability method. P < 0.05 was considered statistically significant.
The metabolic profiles were imported into R for principal component analysis (PCA) to observe the overall distribution among the samples and the stability of the entire analysis process. Partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to distinguish differential metabolites between groups. To prevent overfitting, 7-fold cross-validation and 200 response permutation tests were utilized to evaluate the quality of the model. Variable importance of projection (VIP) values obtained from the OPLS-DA model were used to rank the overall contribution of each variable to group discrimination. A two-tailed Student’s t test was further used to verify whether the differences in metabolites between groups were significant. Differential metabolites were selected with VIP >1.0, P < 0.05, and fold change (FC) >1.5 or <0.7. Binary logistic regression analysis was constructed to screen independent risk factors. Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic ability of differential metabolites between the tested groups.
Cao Y., Liu Z., Ma W., Fang C., Pei Y., Jing Y., Huang J., Han X, & Xiao W. (2023). Untargeted metabolomic profiling of sepsis-induced cardiac dysfunction. Frontiers in Endocrinology, 14, 1060470.
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Publication 2023
Diagnosis Discrimination, Psychology Metabolic Profile Student

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More about "Metabolic Profile"

Metabolic Assessment, Metabolic Evaluation, Metabolic Analysis, Metabolic Profiling, Metabolomics, Metabolic Markers, Metabolic Biomarkers, Metabolic Health, Metabolic Performance, Energy Metabolism, Nutrient Utilization, Waste Management, Metabolic Optimization, Metabolic Disorders, Chronic Conditions, Personalized Interventions, PubCompare.ai, AI-driven Platform, Protocol Comparison, Reproducibility, Research Accuracy, Effective Products, Procedures, Metabolic Efficiency, SIMCA-P Software, SIMCA 14.1, Oligomycin, Triple TOF 5600 System, Seahorse XF96 Extracellular Flux Analyzer, Rotenone, Acquity UHPLC System, GraphPad Prism 5, TargetLynx Application Manager for MassLynx 4.1 Software.