Targeted Metabolomics for Clinical Biomarker Discovery in Multifactorial Diseases

Lundin, Ulrika; Modre-Osprian, Robert; Weinberger, Klaus M.

doi:10.5772/21792

Cited by 3 publications

(19 citation statements)

References 60 publications

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“…5A). The same effect was observed in a cross-sectional study on CKD patients; both in diabetic nephropathy and in non-diabetic CKD, later stages of the disease were significantly associated with higher plasma levels of SDMA (Lundin et al, 2011;Duranton et al, 2014; Fig. 5B).…”

Section: Accepted Manuscriptsupporting

confidence: 79%

“…Specifically, untargeted metabolic profiling of serum, urine, and kidney tissue samples identified tryptophan as a biomarker candidate distinguishing adenine-treated rats from untreated controls (Zhao et al, 2012a;Zhao et al, 2012b;Zhao et al, 2013a;Zhao et al, 2014). Based on a broad and analytically validated quantitative data set, the puromycin model had previously shown the same effect (Robinson et al, 2008;Lundin et al, 2011). Plasma tryptophan levels decreased in a dose-and time-dependent manner in puromycin-treated animals (Fig.…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 89%

“…The continuous loss of albumin in proteinuric animals and patients could, therefore, markedly influence the findings in these studies (given the relatively low affinity -depending on the M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT experimental conditions, the apparent association constant k' is roughly 10 4 , -and the absolute concentrations reported, one must assume that the assays used were measuring total instead of free tryptophan although neither publication specifically comments on this aspect). A matched quantitative analysis of data from the UroSysteOmics study, however, reveals that the fold-change of tryptophan concentrations is much more pronounced than that of albumin levels (-58 % vs. -19 % comparing stage 5 to stage 3; Lundin et al, 2011), so there must be other mechanisms depleting tryptophan.…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 92%

“…Cross-sectional studies on specifically selected patients (Lundin et al, 2011;Duranton, 2014) and on population-based cohorts (Goek et al, 2012) both demonstrated that plasma and serum tryptophan concentrations are significantly lower in patients with a higher stage of CKD (Fig. 7B).…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 93%

“…In animal models of kidney injury such as Sprague-Dawley rats treated with the nephrotoxic antibiotic puromycin, SDMA levels in plasma increase in a dose-and time-dependent manner (Robinson et al, 2008;Lundin et al, 2011;Fig . 5A).…”

Section: Accepted Manuscriptmentioning

confidence: 97%

See 4 more Smart Citations

Metabolic biomarkers for chronic kidney disease

Breit

Weinberger

2016

Archives of Biochemistry and Biophysics

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Section: Accepted Manuscriptsupporting

confidence: 79%

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 89%

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 92%

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 93%

Section: Accepted Manuscriptmentioning

confidence: 97%

See 3 more Smart Citations

Metabolic biomarkers for chronic kidney disease

Breit

Weinberger

2016

Archives of Biochemistry and Biophysics

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Modeling and Classification of Kinetic Patterns of Dynamic Metabolic Biomarkers in Physical Activity

Breit

Netzer

Weinberger³

et al. 2015

PLoS Comput Biol

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The objectives of this work were the classification of dynamic metabolic biomarker candidates and the modeling and characterization of kinetic regulatory mechanisms in human metabolism with response to external perturbations by physical activity. Longitudinal metabolic concentration data of 47 individuals from 4 different groups were examined, obtained from a cycle ergometry cohort study. In total, 110 metabolites (within the classes of acylcarnitines, amino acids, and sugars) were measured through a targeted metabolomics approach, combining tandem mass spectrometry (MS/MS) with the concept of stable isotope dilution (SID) for metabolite quantitation. Biomarker candidates were selected by combined analysis of maximum fold changes (MFCs) in concentrations and P-values resulting from statistical hypothesis testing. Characteristic kinetic signatures were identified through a mathematical modeling approach utilizing polynomial fitting. Modeled kinetic signatures were analyzed for groups with similar behavior by applying hierarchical cluster analysis. Kinetic shape templates were characterized, defining different forms of basic kinetic response patterns, such as sustained, early, late, and other forms, that can be used for metabolite classification. Acetylcarnitine (C2), showing a late response pattern and having the highest values in MFC and statistical significance, was classified as late marker and ranked as strong predictor (MFC = 1.97, P < 0.001). In the class of amino acids, highest values were shown for alanine (MFC = 1.42, P < 0.001), classified as late marker and strong predictor. Glucose yields a delayed response pattern, similar to a hockey stick function, being classified as delayed marker and ranked as moderate predictor (MFC = 1.32, P < 0.001). These findings coincide with existing knowledge on central metabolic pathways affected in exercise physiology, such as β-oxidation of fatty acids, glycolysis, and glycogenolysis. The presented modeling approach demonstrates high potential for dynamic biomarker identification and the investigation of kinetic mechanisms in disease or pharmacodynamics studies using MS data from longitudinal cohort studies.

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Towards Metabolic Biomarkers for the Diagnosis and Prognosis of CKD

Lundin¹,

Weinberger²

2018

Advances in Nephropathy

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Chronic kidney disease, the gradual loss of renal function, is an increasingly recognized burden for patients and health care systems; globally, it has a high and rapidly growing prevalence, a significant mortality, and causes disproportionately high costs, particularly for hemodialysis and kidney transplantations. Yet, the available diagnostic tools are either impractical in clinical routine or have serious shortcomings preventing a well-informed disease management, although optimized treatment strategies with impressive benefits for patients have been established. Advances in bioanalytics have facilitated the identification of many genomic, proteomic, and metabolic biomarker candidates, some of which have been validated in independent cohorts. Summarizing the markers discovered so far, this chapter focuses on compounds or pathways, for which quantitative data, substantiating evidence from translational research, and a mechanistic understanding is available. Also, multiparametric marker panels have been suggested with promising diagnostic and prognostic performance in initial analyses, although the data basis from prospective trials is very limited. Large-scale studies, however, are underway and will validate certain sets of parameters and discard others. Finally, the path from clinical research to routine application is discussed, focusing on potential obstacles such as the use of mass spectrometry, and the feasibility of obtaining regulatory approval for metabolomics assays.

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Targeted Metabolomics for Clinical Biomarker Discovery in Multifactorial Diseases

Cited by 3 publications

References 60 publications

Metabolic biomarkers for chronic kidney disease

Metabolic biomarkers for chronic kidney disease

Modeling and Classification of Kinetic Patterns of Dynamic Metabolic Biomarkers in Physical Activity

Towards Metabolic Biomarkers for the Diagnosis and Prognosis of CKD

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