Potentials and pitfalls of clinical peptidomics and metabolomics

Leichtle, Alexander Benedikt; Dufour, Jean–François; Fiedler, Georg Martin

doi:10.4414/smw.2013.13801

Cited by 25 publications

(30 citation statements)

References 87 publications

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“…There is interest in metabolomics of RA, and both NMR and MS have been successfully used to differentiate RA from other inflammatory diseases and from controls. The next steps will likely involve further integration of RA metabolomics data onto the human metabolic network, such as Recon 2 [98,99]; this integration is seen by some as the greatest challenge in using members of the 'omics' family [100].…”

Section: Resultsmentioning

confidence: 99%

Lipid and Metabolic Changes in Rheumatoid Arthritis

McGrath

Young

2015

Curr Rheumatol Rep

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While the most obvious manifestations of rheumatoid arthritis (RA) involve inflammation and damage in the synovial joints, the systemic effects of the condition are widespread and life-threatening. Of particular interest is the 'lipid paradox' of RA, where patients with a numerically equivocal starting lipid profile have a significantly raised risk of cardiovascular (CV) events and response to therapy results in a 'normalization' of lipid levels and reduction in events. Changes in lipids can be seen before overt disease manifestations which suggest that they are closely linked to the more widespread inflammation-driven metabolic changes associated with tumour necrosis factor (TNF). Cachexia involves a shift in body mass from muscle to fat, which may or may not directly increase the cardiovascular risk. However, since TNF inhibition is associated with reduction in cardiovascular events, it does suggest that these widespread metabolic changes involving lipids are of importance. Analysis of single lipids or metabolites have been used to identify some of the key changes, but more recently, metabolomic and lipidomic approaches have been applied to identify a broad spectrum of small molecule changes and identify potentially altered metabolic pathways. Further work is needed to understand fully the metabolic changes in lipid profiles and identify novel ways of targeting desired profile changes, but work so far does suggest that a better understanding may allow management of patients to downregulate the systemic effects of their disease that puts them at risk of cardiovascular complications.

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Section: Resultsmentioning

confidence: 99%

Lipid and Metabolic Changes in Rheumatoid Arthritis

McGrath

Young

2015

Curr Rheumatol Rep

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“…The continually increasing number of metabolomics applications in clinical research has led to an increasing demand for preanalytical SOPs to standardize the collection, transportation, preparation, and storage of clinical samples [108]. Such SOPs have been commonly used during clinical trials [24] and they are of great importance for targeted and nontargeted clinical metabolomics studies [25, 107, 109].…”

Section: Preanalytical Sop For Blood In Metabolomicsmentioning

confidence: 99%

Effects of pre-analytical processes on blood samples used in metabolomics studies

2015

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Every day, analytical and bio-analytical chemists make sustained efforts to improve the sensitivity, specificity, robustness, and reproducibility of their methods. Especially in targeted and non-targeted profiling approaches, including metabolomics analysis, these objectives are not easy to achieve; however, robust and reproducible measurements and low coefficients of variation (CV) are crucial for successful metabolomics approaches. Nevertheless, all efforts from the analysts are in vain if the sample quality is poor, i.e. if preanalytical errors are made by the partner during sample collection. Preanalytical risks and errors are more common than expected, even when standard operating procedures (SOP) are used. This risk is particularly high in clinical studies, and poor sample quality may heavily bias the CV of the final analytical results, leading to disappointing outcomes of the study and consequently, although unjustified, to critical questions about the analytical performance of the approach from the partner who provided the samples. This review focuses on the preanalytical phase of liquid chromatography–mass spectrometry-driven metabolomics analysis of body fluids. Several important preanalytical factors that may seriously affect the profile of the investigated metabolome in body fluids, including factors before sample collection, blood drawing, subsequent handling of the whole blood (transportation), processing of plasma and serum, and inadequate conditions for sample storage, will be discussed. In addition, a detailed description of latent effects on the stability of the blood metabolome and a suggestion for a practical procedure to circumvent risks in the preanalytical phase will be given.Graphical AbstractThe procedures and potential problems in preanalytical aspects of metabolomics studies using blood samples. Bias in the preanalytical phase may lead to unwanted results in the subsequential studies

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“…Validation of the candidate biomarkers represents the last and essential step of the process, confirming the clinical utility. The purpose of the biomarkers (predictive, prognosis or diagnostic markers) will determine the sensitivity and specificity required for clinical applications [136]. The potential pitfalls that may come up during the validation stage are similar to the ones encountered in the biomarker discovery stage.…”

Section: Metabolite Identification and Biological Interpretationmentioning

confidence: 95%

Analytical Pitfalls and Challenges in Clinical Metabolomics

et al. 2016

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Metabolomics-based strategies have become an integral part of modern clinical research, allowing for a better understanding of pathophysiological conditions and disease mechanisms, as well as providing innovative tools for more adequate diagnostic and prognosis approaches. Metabolomics is considered an essential tool in precision medicine, which aims for personalized prevention and tailor-made treatments. Nevertheless, multiple pitfalls may be encountered in clinical metabolomics during the entire workflow, hampering the quality of the data and, thus, the biological interpretation. This review describes the challenges underlying metabolomics-based experiments, discussing step by step the potential pitfalls of the analytical process, including study design, sample collection, storage, as well as preparation, chromatographic and electrophoretic separation, detection and data analysis. Moreover, it offers practical solutions and strategies to tackle these challenges, ensuring the generation of high-quality data.

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Potentials and pitfalls of clinical peptidomics and metabolomics

Cited by 25 publications

References 87 publications

Lipid and Metabolic Changes in Rheumatoid Arthritis

Lipid and Metabolic Changes in Rheumatoid Arthritis

Effects of pre-analytical processes on blood samples used in metabolomics studies

Analytical Pitfalls and Challenges in Clinical Metabolomics

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