Clinical Bioinformatics for Biomarker Discovery in Targeted Metabolomics

Breit, Marc; Baumgartner, Christian; Netzer, Michael; Weinberger, Klaus M.

doi:10.1007/978-94-017-7543-4_8

Cited by 4 publications

(5 citation statements)

References 147 publications

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“…There are numerous definitions of clinical bioinformatics or related terms (for example, see [12]). Our perception of what this field entails is that by enabling the bioinformatics mining and use of “omics” and other high-throughput data in a clinical setting, by integrating various standardized and interoperable datasets, by extracting valuable clinically useful medical knowledge from these data resources, and by providing clinical-grade analyses or decision-support tools, clinical bioinformatics bridges the gap between medical practitioners and the fruits of biological research.…”

Section: The Role Of Clinical Bioinformatics In Precision Medicinementioning

confidence: 99%

Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities

2016

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This era of groundbreaking scientific developments in high-resolution, high-throughput technologies is allowing the cost-effective collection and analysis of huge, disparate datasets on individual health. Proper data mining and translation of the vast datasets into clinically actionable knowledge will require the application of clinical bioinformatics. These developments have triggered multiple national initiatives in precision medicine—a data-driven approach centering on the individual. However, clinical implementation of precision medicine poses numerous challenges. Foremost, precision medicine needs to be contrasted with the powerful and widely used practice of evidence-based medicine, which is informed by meta-analyses or group-centered studies from which mean recommendations are derived. This “one size fits all” approach can provide inadequate solutions for outliers. Such outliers, which are far from an oddity as all of us fall into this category for some traits, can be better managed using precision medicine. Here, we argue that it is necessary and possible to bridge between precision medicine and evidence-based medicine. This will require worldwide and responsible data sharing, as well as regularly updated training programs. We also discuss the challenges and opportunities for achieving clinical utility in precision medicine. We project that, through collection, analyses and sharing of standardized medically relevant data globally, evidence-based precision medicine will shift progressively from therapy to prevention, thus leading eventually to improved, clinician-to-patient communication, citizen-centered healthcare and sustained well-being.

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Section: The Role Of Clinical Bioinformatics In Precision Medicinementioning

confidence: 99%

Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities

2016

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“…Thanks to an elaborate body of knowledge in biochemistry, a peculiarity within the process of data analysis in metabolomics lies in the dedicated biochemical interpretation of results [ 54 ]. This knowledge is nowadays annotated in public databases, e.g., the Kyoto Encyclopedia of Genes and Genomes (KEGG) [ 55 ], and eases the interpretation of findings in the context of annotated biochemical pathways.…”

Section: Resultsmentioning

confidence: 99%

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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“…As advances in analytical instrumentation provide an ever-growing array of chemical identifications and improved sensitivities, interpreting the meaning of the presence and amounts of these chemicals is becoming increasingly difficult (Breit et al 2016;Lianidou and Markou 2011;Moons et al 2012;Pierce et al 2008;Rubino et al 2009). Over the past few years, investigators developed a series of data visualization and statistical approaches for extracting meaning from such complex data based upon heat map graphics and multivariate analyses of high-resolution mass spectrometry data (Pleil and Isaacs 2016;Pleil et al 2011cPleil et al , 2011bSobus et al 2010a).…”

Section: Data Complexitymentioning

confidence: 99%

Human biomarker interpretation: the importance of intra-class correlation coefficients (ICC) and their calculations based on mixed models, ANOVA, and variance estimates

Pleil

Wallace

Stiegel

et al. 2018

Journal of Toxicology and Environmental Health, Part B

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Human biomonitoring is the foundation of environmental toxicology, community public health evaluation, preclinical health effects assessments, pharmacological drug development and testing, and medical diagnostics. Within this framework, the intra-class correlation coefficient (ICC) serves as an important tool for gaining insight into human variability and responses and for developing risk-based assessments in the face of sparse or highly complex measurement data. The analytical procedures that provide data for clinical and public health efforts are continually evolving to expand our knowledge base of the many thousands of environmental and biomarker chemicals that define human systems biology. These chemicals range from the smallest molecules from energy metabolism (i.e., the metabolome), through larger molecules including enzymes, proteins, RNA, DNA, and adducts. In additiona, the human body contains exogenous environmental chemicals and contributions from the microbiome from gastrointestinal, pulmonary, urogenital, naso-pharyngeal, and skin sources. This complex mixture of biomarker chemicals from environmental, human, and microbiotic sources comprise the human exposome and generally accessed through sampling of blood, breath, and urine. One of the most difficult problems in biomarker assessment is assigning probative value to any given set of measurements as there are generally insufficient data to distinguish among sources of chemicals such as environmental, microbiotic, or human metabolism and also deciding which measurements are remarkable from those that are within normal human variability. The implementation of longitudinal (repeat) measurement strategies has provided new statistical approaches for interpreting such complexities, and use of descriptive statistics based upon intra-class correlation coefficients (ICC) has become a powerful tool in these efforts. This review has two parts; the first focuses on the history of repeat measures of human biomarkers starting with occupational toxicology of the early 1950s through modern applications in interpretation of the human exposome and metabolic adverse outcome pathways (AOPs). The second part reviews different methods for calculating the ICC and explores the strategies and applications in light of different data structures.

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Clinical Bioinformatics for Biomarker Discovery in Targeted Metabolomics

Cited by 4 publications

References 147 publications

Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities

Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities

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

Human biomarker interpretation: the importance of intra-class correlation coefficients (ICC) and their calculations based on mixed models, ANOVA, and variance estimates

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