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Individual differences in metabolomics: individualised responses and between-metabolite relationships.

ABSTRACT: Many metabolomics studies aim to find 'biomarkers': sets of molecules that are consistently elevated or decreased upon experimental manipulation. Biological effects, however, often manifest themselves along a continuum of individual differences between the biological replicates in the experiment. Such differences are overlooked or even diminished by methods in standard use for metabolomics, although they may contain a wealth of information on the experiment. Properly understanding individual differences is crucial for generating knowledge in fields like personalised medicine, evolution and ecology. We propose to use simultaneous component analysis with individual differences constraints (SCA-IND), a data analysis method from psychology that focuses on these differences. This method constructs axes along the natural biochemical differences between biological replicates, comparable to principal components. The model may shed light on changes in the individual differences between experimental groups, but also on whether these differences correspond to, e.g., responders and non-responders or to distinct chemotypes. Moreover, SCA-IND reveals the individuals that respond most to a manipulation and are best suited for further experimentation. The method is illustrated by the analysis of individual differences in the metabolic response of cabbage plants to herbivory. The model reveals individual differences in the response to shoot herbivory, where two 'response chemotypes' may be identified. In the response to root herbivory the model shows that individual plants differ strongly in response dynamics. Thereby SCA-IND provides a hitherto unavailable view on the chemical diversity of the induced plant response, that greatly increases understanding of the system. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-012-0414-8) contains supplementary material, which is available to authorized users.

SUBMITTER: Jansen JJ

PROVIDER: S-EPMC3337417 | biostudies-other | 2012 Jun

REPOSITORIES: biostudies-other

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Individual differences in metabolomics: individualised responses and between-metabolite relationships.

Jansen Jeroen J JJ Szymańska Ewa E Hoefsloot Huub C J HC Smilde Age K AK

Metabolomics : Official journal of the Metabolomic Society 20120315 Suppl 1

Many metabolomics studies aim to find 'biomarkers': sets of molecules that are consistently elevated or decreased upon experimental manipulation. Biological effects, however, often manifest themselves along a continuum of individual differences between the biological replicates in the experiment. Such differences are overlooked or even diminished by methods in standard use for metabolomics, although they may contain a wealth of information on the experiment. Properly understanding individual dif ...[more]

PMID: 22593723

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Project description:BackgroundHuman plasma and serum are widely used matrices in clinical and biological studies. However, different collecting procedures and the coagulation cascade influence concentrations of both proteins and metabolites in these matrices. The effects on metabolite concentration profiles have not been fully characterized.Methodology/principal findingsWe analyzed the concentrations of 163 metabolites in plasma and serum samples collected simultaneously from 377 fasting individuals. To ensure data quality, 41 metabolites with low measurement stability were excluded from further analysis. In addition, plasma and corresponding serum samples from 83 individuals were re-measured in the same plates and mean correlation coefficients (r) of all metabolites between the duplicates were 0.83 and 0.80 in plasma and serum, respectively, indicating significantly better stability of plasma compared to serum (p?=?0.01). Metabolite profiles from plasma and serum were clearly distinct with 104 metabolites showing significantly higher concentrations in serum. In particular, 9 metabolites showed relative concentration differences larger than 20%. Despite differences in absolute concentration between the two matrices, for most metabolites the overall correlation was high (mean r?=?0.81±0.10), which reflects a proportional change in concentration. Furthermore, when two groups of individuals with different phenotypes were compared with each other using both matrices, more metabolites with significantly different concentrations could be identified in serum than in plasma. For example, when 51 type 2 diabetes (T2D) patients were compared with 326 non-T2D individuals, 15 more significantly different metabolites were found in serum, in addition to the 25 common to both matrices.Conclusions/significanceOur study shows that reproducibility was good in both plasma and serum, and better in plasma. Furthermore, as long as the same blood preparation procedure is used, either matrix should generate similar results in clinical and biological studies. The higher metabolite concentrations in serum, however, make it possible to provide more sensitive results in biomarker detection.

Project description:Background:Good sibling relationships in adulthood are known to be a protective factor for mental health. The quality of these relationships is influence by the sibship's inherent characteristics (e.g., birth order, number of brothers and sisters, sex composition, age gaps). The present study explored whether these same determinants can help to explain how individuals experience their relationship with a sibling who has been diagnosed with schizophrenia. Method:A total of 374 adults completed the Adult Sibling Relationship Questionnaire, a scale that probes the quality of these relationships on three dimensions: warmth, conflict, and rivalry. We also collected sociodemographic data and information about family structure from each of the participants. Participants were divided into two matched groups: nonclinical sibling group (n = 187) or schizophrenia sibling group (n = 187). Regression analyses were conducted to extract possible predictors of sibling relationship quality for each group. Further regression analyses then focused exclusively on relationships with an ill sibling, in order to study the role of disease-related variables in explaining each of the three dimensions. Results:Results showed that sociodemographic and family structure data explained a significant proportion of variance in the sibling relationship, but solely for nonclinical siblings. When participants had a sibling with schizophrenia, we found that disease-related variables (symptom severity, frequency of treatment) also had to be included to predict the conflict dimension. Conclusions:Our results suggest that feelings of conflict experienced by the schizophrenia sibling group were fueled by the symptoms the ill person displayed. Healthy brothers and sisters probably have only a poor understanding of these symptoms. This could be improved by supporting them and helping them learn more about the disease. Future research will have to prove that providing such support for siblings does indeed improve the quality of their sibling relationships and, by so doing, enhance the wellbeing of both members of a sibling dyad.

Project description:ImportanceWhile "omics" studies have advanced our understanding of inflammatory skin diseases, metabolomics is mostly an unexplored field in dermatology.ObjectiveWe sought to elucidate the pathogenesis of psoriatic diseases by determining the differences in metabolomic profiles among psoriasis patients with or without psoriatic arthritis and healthy controls.DesignWe employed a global metabolomics approach to compare circulating metabolites from patients with psoriasis, psoriasis and psoriatic arthritis, and healthy controls.SettingStudy participants were recruited from the general community and from the Psoriasis Clinic at the University of California Davis in United States.ParticipantsWe examined metabolomic profiles using blood serum samples from 30 patients age and gender matched into three groups: 10 patients with psoriasis, 10 patients with psoriasis and psoriatic arthritis and 10 control participants. Main outcome(s) and measures(s): Metabolite levels were measured calculating the mean peak intensities from gas chromatography time-of-flight mass spectrometry.ResultsMultivariate analyses of metabolomics profiles revealed altered serum metabolites among the study population. Compared to control patients, psoriasis patients had a higher level of alpha ketoglutaric acid (Pso: 288 ± 88;Control209 ± 69; p=0.03), a lower level of asparagine (Pso: 5460 ± 980;Control7260 ± 2100; p=0.02), and a lower level of glutamine (Pso: 86000 ± 20000;Control111000 ± 27000; p=0.02). Compared to control patients, patients with psoriasis and psoriatic arthritis had increased levels of glucuronic acid (Pso + PsA: 638 ± 250;Control347 ± 61; p=0.001). Compared to patients with psoriasis alone, patients with both psoriasis and psoriatic arthritis had a decreased level of alpha ketoglutaric acid (Pso + PsA: 186 ± 80; Pso: 288 ± 88; p=0.02) and an increased level of lignoceric acid (Pso + PsA: 442 ± 280; Pso: 214 ± 64; p=0.02).Conclusions and relevanceThe metabolite differences help elucidate the pathogenesis of psoriasis and psoriatic arthritis and they may provide insights for therapeutic development.

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Individual differences in metabolomics: individualised responses and between-metabolite relationships.

Publications

Individual differences in metabolomics: individualised responses and between-metabolite relationships.

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